图像滤镜艺术—换脸算法资源收集

https://github.com/Guikunzhi/BeautifyFaceDemo

https://github.com/alyssaq/face_morpher

http://life-in-a-monospace-typeface.tumblr.com/post/28495219189/quick-and-dirty-automatic-face-replacement-with

Image Morphing - local (non-parametric) warp
http://alumni.media.mit.edu/~maov/classes/comp_photo_vision08f/lect/07_Image%20Morphing.pdf

https://github.com/arturoc/FaceSubstitution
https://github.com/jorticus/face-replace

https://github.com/t0nyren/piecewiseAffine

https://github.com/royshil/HeadReplacement

https://github.com/trishume/faceHack

https://github.com/marsbroshok/face-replace

https://github.com/junyanz/FaceDemo

https://github.com/matthewearl/faceswap

http://home.elka.pw.edu.pl/%7Emkowals6/doku.php

https://github.com/takiyu/CLM

https://github.com/mc-jesus/FaceSwap

https://matthewearl.github.io/2015/07/28/switching-eds-with-python/

https://github.com/MarekKowalski/FaceSwap

http://alumni.media.mit.edu/~roys/identitytransfer-cgaieee2012/

https://github.com/HVisionSensing/FaceFlip

https://github.com/liaojing/Image-Morphing/tree/master/code

https://github.com/blendmaster/rigid-faces

https://github.com/hrastnik/FaceSwap

https://github.com/spmallick/learnopencv/tree/master/FaceMorph
https://github.com/spmallick/learnopencv/tree/master/FaceSwap

https://github.com/YuvalNirkin/face_swap

https://github.com/menpo/lsfm

#Deep learning
https://github.com/ddtm/deep-smile-warp
https://github.com/msracver/Deep-Image-Analogy
https://github.com/datitran/face2face-demo
https://github.com/ZZUTK/Face-Aging-CAAE
https://github.com/zo7/deconvfaces

3D
https://github.com/KeeganRen/FaceReconstruction
https://github.com/anhttran/3dmm_cnn

Video:
https://github.com/YuvalNirkin/face_video_segment


DeepFix: A Fully Convolutional Neural Network for predicting Human Eye Fixations
    
     arxiv: http://arxiv.org/abs/1510.02927
    
Some like it hot - visual guidance for preference prediction
    
     arxiv: http://arxiv.org/abs/1510.07867
     demo: http://howhot.io/
    
Deep Learning Algorithms with Applications to Video Analytics for A Smart City: A Survey
    
     arxiv: http://arxiv.org/abs/1512.03131
    
Deep Relative Attributes
    
     intro: ACCV 2016
     arxiv: http://arxiv.org/abs/1512.04103
     github: https://github.com/yassersouri/ghiaseddin
    
Deep-Spying: Spying using Smartwatch and Deep Learning
    
     arxiv: http://arxiv.org/abs/1512.05616
     github: https://github.com/tonybeltramelli/Deep-Spying
    
Camera identification with deep convolutional networks
    
     key word: copyright infringement cases, ownership attribution
     arxiv: http://arxiv.org/abs/1603.01068
    
An Analysis of Deep Neural Network Models for Practical Applications
    
     arxiv: http://arxiv.org/abs/1605.07678
    
8 Inspirational Applications of Deep Learning
    
     intro: Colorization of Black and White Images, Adding Sounds To Silent Movies, Automatic Machine Translation Object Classification in Photographs, Automatic Handwriting Generation, Character Text Generation, Image Caption Generation, Automatic Game Playing
     blog: http://machinelearningmastery.com/inspirational-applications-deep-learning/
    
16 Open Source Deep Learning Models Running as Microservices
    
     intro: Places 365 Classifier, Deep Face Recognition, Real Estate Classifier, Colorful Image Colorization, Illustration Tagger, InceptionNet, Parsey McParseface, ArtsyNetworks
     blog: http://blog.algorithmia.com/2016/07/open-source-deep-learning-algorithm-roundup/
    
Deep Cascaded Bi-Network for Face Hallucination
    
     project page: http://mmlab.ie.cuhk.edu.hk/projects/CBN.html
     arxiv: http://arxiv.org/abs/1607.05046
    
DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation

    
     project page: http://yaroslav.ganin.net/static/deepwarp/
     arxiv: http://arxiv.org/abs/1607.07215
    
Autoencoding Blade Runner
    
     blog: https://medium.com/@Terrybroad/autoencoding-blade-runner-88941213abbe#.9kckqg7cq
     github: https://github.com/terrybroad/Learned-Sim-Autoencoder-For-Video-Frames
    
A guy trained a machine to "watch" Blade Runner. Then things got seriously sci-fi.
http://www.vox.com/2016/6/1/11787262/blade-runner-neural-network-encoding
Deep Convolution Networks for Compression Artifacts Reduction

    
     intro: ICCV 2015
     project page(code): http://mmlab.ie.cuhk.edu.hk/projects/ARCNN.html
     arxiv: http://arxiv.org/abs/1608.02778
    
Deep GDashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks
    
     intro: Deep Genomic Dashboard (Deep GDashboard)
     arxiv: http://arxiv.org/abs/1608.03644
    
Instagram photos reveal predictive markers of depression
    
     arxiv: http://arxiv.org/abs/1608.03282
    
How an Algorithm Learned to Identify Depressed Individuals by Studying Their Instagram Photos
    
     review: https://www.technologyreview.com/s/602208/how-an-algorithm-learned-to-identify-depressed-individuals-by-studying-their-instagram/
    
IM2CAD
    
     arxiv: http://arxiv.org/abs/1608.05137
    
Fast, Lean, and Accurate: Modeling Password Guessability Using Neural Networks
    
     paper: https://www.usenix.org/conference/usenixsecurity16/technical-sessions/presentation/melicher
     github: https://github.com/cupslab/neural_network_cracking
    
Defeating Image Obfuscation with Deep Learning
    
     arxiv: http://arxiv.org/abs/1609.00408
    
Detecting Music BPM using Neural Networks

    
     keywords: BPM (Beats Per Minutes)
     blog: https://nlml.github.io/neural-networks/detecting-bpm-neural-networks/
     github: https://github.com/nlml/bpm
    
Generative Visual Manipulation on the Natural Image Manifold

    
     intro: ECCV 2016
     project page: https://people.eecs.berkeley.edu/~junyanz/projects/gvm/
     arxiv: http://arxiv.org/abs/1609.03552
     github: https://github.com/junyanz/iGAN
    
Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition
    
     arxiv: http://arxiv.org/abs/1609.05119
    
Deep Gold: Using Convolution Networks to Find Minerals
    
     blog: https://hackernoon.com/deep-gold-using-convolution-networks-to-find-minerals-aafdb37355df#.lgh95ub4a
     github: https://github.com/scottvallance/DeepGold
    
Predicting First Impressions with Deep Learning
    
     arxiv: https://arxiv.org/abs/1610.08119
    
Judging a Book By its Cover
    
     arxiv: https://arxiv.org/abs/1610.09204
     review: https://www.technologyreview.com/s/602807/deep-neural-network-learns-to-judge-books-by-their-covers/
    
Image Credibility Analysis with Effective Domain Transferred Deep Networks
    
     arxiv: https://arxiv.org/abs/1611.05328
    
A novel image tag completion method based on convolutional neural network
    
     arxiv: https://www.arxiv.org/abs/1703.00586
    
Image operator learning coupled with CNN classification and its application to staff line removal
    
     intro: ICDAR 2017
     arxiv: https://arxiv.org/abs/1709.06476
    
Joint Image Filtering with Deep Convolutional Networks
    
     intro: University of California, Merced & Virginia Tech & University of Illinois
     project page: http://vllab1.ucmerced.edu/~yli62/DJF_residual/
     arxiv: https://arxiv.org/abs/1710.04200
     github: https://github.com/Yijunmaverick/DeepJointFilter
    
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
    
     intro: ICCV 2017
     arxiv: https://arxiv.org/abs/1704.02470
     github: https://github.com/aiff22/DPED
    

      Image-Text
Learning Two-Branch Neural Networks for Image-Text Matching Tasks
https://arxiv.org/abs/1704.03470
Dual-Path Convolutional Image-Text Embedding
https://arxiv.org/abs/1711.05535

      Age Estimation
Deeply-Learned Feature for Age Estimation
    
     paper: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=7045931&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D7045931
    
Age and Gender Classification using Convolutional Neural Networks
    
     paper: http://www.openu.ac.il/home/hassner/projects/cnn_agegender/CNN_AgeGenderEstimation.pdf
     project page: http://www.openu.ac.il/home/hassner/projects/cnn_agegender/
     github: https://github.com/GilLevi/AgeGenderDeepLearning
    
Group-Aware Deep Feature Learning For Facial Age Estimation
    
     paper: http://www.sciencedirect.com/science/article/pii/S0031320316303417
    
Local Deep Neural Networks for Age and Gender Classification
https://arxiv.org/abs/1703.08497
Understanding and Comparing Deep Neural Networks for Age and Gender Classification
https://arxiv.org/abs/1708.07689
Age Group and Gender Estimation in the Wild with Deep RoR Architecture
    
     intro: IEEE ACCESS
     arxiv: https://arxiv.org/abs/1710.02985
    

      Face Aging
Recurrent Face Aging
    
     paper: www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Wang_Recurrent_Face_Aging_CVPR_2016_paper.pdf
    
Face Aging With Conditional Generative Adversarial Networks
    
     arxiv: https://arxiv.org/abs/1702.01983
    

      Emotion Recognition / Expression Recognition
Real-time emotion recognition for gaming using deep convolutional network features
    
     paper: http://arxiv.org/abs/1408.3750v1
     code: https://github.com/Zebreu/ConvolutionalEmotion
    
Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns
    
     project page: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/
     paper: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/LeviHassnerICMI15.pdf
     github: https://gist.github.com/GilLevi/54aee1b8b0397721aa4b
     blog: https://gilscvblog.com/2017/01/31/emotion-recognition-in-the-wild-via-convolutional-neural-networks-and-mapped-binary-patterns/
    
DeXpression: Deep Convolutional Neural Network for Expression Recognition
    
     paper: http://arxiv.org/abs/1509.05371
    
DEX: Deep EXpectation of apparent age from a single image

    
     intro: ICCV 2015
     paper: https://www.vision.ee.ethz.ch/en/publications/papers/proceedings/eth_biwi_01229.pdf
     homepage: https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/
    
EmotioNet: EmotioNet: An accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild
    
     intro: CVPR 2016
     paper: http://cbcsl.ece.ohio-state.edu/cvpr16.pdf
     database: http://cbcsl.ece.ohio-state.edu/dbform_emotionet.html
    
How Deep Neural Networks Can Improve Emotion Recognition on Video Data
    
     intro: ICIP 2016
     arxiv: http://arxiv.org/abs/1602.07377
    
Peak-Piloted Deep Network for Facial Expression Recognition
    
     arxiv: http://arxiv.org/abs/1607.06997
    
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
    
     arxiv: http://arxiv.org/abs/1608.01041
    
A Recursive Framework for Expression Recognition: From Web Images to Deep Models to Game Dataset
    
     arxiv: http://arxiv.org/abs/1608.01647
    
FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition
    
     arxiv: http://arxiv.org/abs/1609.06591
    
EmotionNet Challenge
    
     homrepage: http://cbcsl.ece.ohio-state.edu/EmotionNetChallenge/index.html
     dataset: http://cbcsl.ece.ohio-state.edu/dbform_emotionet.html
    
Baseline CNN structure analysis for facial expression recognition
    
     intro: RO-MAN2016 Conference
     arxiv: https://arxiv.org/abs/1611.04251
    
Facial Expression Recognition using Convolutional Neural Networks: State of the Art
    
     arxiv: https://arxiv.org/abs/1612.02903
    
DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network
    
     arxiv: https://arxiv.org/abs/1702.04280
     api: https://www.sighthound.com/products/cloud
    
Deep generative-contrastive networks for facial expression recognition
https://arxiv.org/abs/1703.07140
Convolutional Neural Networks for Facial Expression Recognition
https://arxiv.org/abs/1704.06756
End-to-End Multimodal Emotion Recognition using Deep Neural Networks
    
     intro: Imperial College London
     arxiv: https://arxiv.org/abs/1704.08619
    
Spatial-Temporal Recurrent Neural Network for Emotion Recognition
https://arxiv.org/abs/1705.04515
Facial Emotion Detection Using Convolutional Neural Networks and Representational Autoencoder Units
https://arxiv.org/abs/1706.01509
Temporal Multimodal Fusion for Video Emotion Classification in the Wild
https://arxiv.org/abs/1709.07200
Island Loss for Learning Discriminative Features in Facial Expression Recognition
https://arxiv.org/abs/1710.03144
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://arxiv.org/abs/1710.07557

      Attribution Prediction
PANDA: Pose Aligned Networks for Deep Attribute Modeling
    
     intro: Facebook. CVPR 2014
     arxiv: http://arxiv.org/abs/1311.5591
     github: https://github.com/facebook/pose-aligned-deep-networks
    
Predicting psychological attributions from face photographs with a deep neural network
    
     arxiv: http://arxiv.org/abs/1512.01289
    
Learning Human Identity from Motion Patterns
    
     arxiv: http://arxiv.org/abs/1511.03908
    

      Pose Estimation
DeepPose: Human Pose Estimation via Deep Neural Networks
    
     intro: CVPR 2014
     arxiv: http://arxiv.org/abs/1312.4659
     slides: http://140.122.184.143/paperlinks/Slides/DeepPose_HumanPose_Estimation_via_Deep_Neural_Networks.pptx
     github: https://github.com/asanakoy/deeppose_tf
    
Heterogeneous multi-task learning for human pose estimation with deep convolutional neural network
    
     paper: www.cv-foundation.org/openaccess/content_cvpr_workshops_2014/W15/papers/LI_Heterogeneous_Multi-task_Learning_2014_CVPR_paper.pdf
    
Flowing ConvNets for Human Pose Estimation in Videos
    
     arxiv: http://arxiv.org/abs/1506.02897
     homepage: http://www.robots.ox.ac.uk/~vgg/software/cnn_heatmap/
     github: https://github.com/tpfister/caffe-heatmap
    
Structured Feature Learning for Pose Estimation
    
     arxiv: http://arxiv.org/abs/1603.09065
     homepage: http://www.ee.cuhk.edu.hk/~xgwang/projectpage_structured_feature_pose.html
    
Convolutional Pose Machines
    
     arxiv: http://arxiv.org/abs/1602.00134
     github: https://github.com/shihenw/convolutional-pose-machines-release
     github(PyTorch): https://github.com/tensorboy/pytorch_Realtime_Multi-Person_Pose_Estimation
    
Model-based Deep Hand Pose Estimation
    
     paper: http://xingyizhou.xyz/zhou2016model.pdf
     github: https://github.com/tenstep/DeepModel
    
Stacked Hourglass Networks for Human Pose Estimation
    
     homepage: http://www-personal.umich.edu/~alnewell/pose/
     arxiv: http://arxiv.org/abs/1603.06937
     github: https://github.com/anewell/pose-hg-train
     demo: https://github.com/anewell/pose-hg-demo
    
Chained Predictions Using Convolutional Neural Networks
    
     intro: EECV 2016
     keywords: CNN, structured prediction, RNN, human pose estimation
     arxiv: http://arxiv.org/abs/1605.02346
    
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
    
     arxiv: http://arxiv.org/abs/1605.03170
     github: https://github.com/eldar/deepcut-cnn
    
Real-time Human Pose Estimation from Video with Convolutional Neural Networks
    
     arxiv: http://arxiv.org/abs/1609.07420
    
Region Ensemble Network: Improving Convolutional Network for Hand Pose Estimation
    
     arxiv: https://arxiv.org/abs/1702.02447
    
Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources
    
     project page: https://www.adrianbulat.com/binary-cnn-landmarks
     arxiv: https://www.arxiv.org/abs/1703.00862
    
Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation
    
     arxiv: https://arxiv.org/abs/1705.00389
     video: http://v.qq.com/x/page/c039862eira.html
     video: http://v.qq.com/x/page/f0398zcvkl5.html
     video: http://v.qq.com/x/page/w0398ei9m1r.html
    
Human Pose Detection Mining Body Language from Videos
    
     blog: https://medium.com/@samim/human-pose-detection-51268e95ddc2
    
OpenPose: A Real-Time Multi-Person Keypoint Detection And Multi-Threading C++ Library
    
     intro: OpenPose is a library for real-time multi-person keypoint detection and multi-threading written in C++ using OpenCV and Caffe
     github: https://github.com/CMU-Perceptual-Computing-Lab/openpose
    
Learning Feature Pyramids for Human Pose Estimation
    
     arxiv: https://arxiv.org/abs/1708.01101
     github: https://github.com/bearpaw/PyraNet
    
Multi-Context Attention for Human Pose Estimation
    
     intro: CVPR 2017
     arxiv: https://arxiv.org/abs/1702.07432
     github(Torch): https://github.com/bearpaw/pose-attention
    
Human Pose Estimation with TensorFlow
https://github.com/eldar/pose-tensorflow
Cascaded Pyramid Network for Multi-Person Pose Estimation
    
     intro: Tsinghua University & HuaZhong Univerisity of Science and Technology & Megvii Inc
     github: https://arxiv.org/abs/1711.07319
    

      Sentiment Analysis / Sentiment Prediction
From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction
    
     arxiv: http://arxiv.org/abs/1604.03489
     github: https://github.com/imatge-upc/sentiment-2016
     gitxiv: http://gitxiv.com/posts/ruqRgXdPTHJ77LDEb/from-pixels-to-sentiment-fine-tuning-cnns-for-visual
    
Predict Sentiment From Movie Reviews Using Deep Learning
    
     blog: http://machinelearningmastery.com/predict-sentiment-movie-reviews-using-deep-learning/
    
Neural Sentiment Classification with User and Product Attention
    
     intro: EMNLP 2016
     paper: http://www.thunlp.org/~chm/publications/emnlp2016_NSCUPA.pdf
     github: https://github.com/thunlp/NSC
    
From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction
    
     intro: Accepted for publication in Image and Vision Computing
     arxiv: https://arxiv.org/abs/1604.03489
     github: https://github.com/imatge-upc/sentiment-2016
    
Visual and Textual Sentiment Analysis Using Deep Fusion Convolutional Neural Networks
    
     intro: ICIP 2017
     arxiv: https://arxiv.org/abs/1711.07798
    

      Place Recognition
NetVLAD: CNN architecture for weakly supervised place recognition

    
     intro: CVPR 2016
     intro: Google Street View Time Machine, soft-assignment, Weakly supervised triplet ranking loss
     homepage: http://www.di.ens.fr/willow/research/netvlad/
     arxiv: http://arxiv.org/abs/1511.07247
    
PlaNet - Photo Geolocation with Convolutional Neural Networks

    
     arxiv: http://arxiv.org/abs/1602.05314
     review("Google Unveils Neural Network with “Superhuman” Ability to Determine the Location of Almost Any Image"): https://www.technologyreview.com/s/600889/google-unveils-neural-network-with-superhuman-ability-to-determine-the-location-of-almost/
     github("City-Recognition: CS231n Project for Winter 2016"): https://github.com/dmakian/LittlePlaNet
     github: https://github.com/wulfebw/LittlePlaNet-Models
    
Visual place recognition using landmark distribution descriptors
    
     arxiv: http://arxiv.org/abs/1608.04274
    
Low-effort place recognition with WiFi fingerprints using deep learning
    
     arxiv: https://arxiv.org/abs/1611.02049
     github: https://github.com/aqibsaeed/Place-Recognition-using-Autoencoders-and-NN
     github(Keras): https://github.com/mallsk23/place_recognition_wifi_fingerprints_deep_learning
    
Deep Learning Features at Scale for Visual Place Recognition
    
     intro: ICRA 2017
     arxiv: https://arxiv.org/abs/1701.05105
    
Place recognition: An Overview of Vision Perspective
https://arxiv.org/abs/1707.03470

      Camera Relocalization
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
    
     paper: http://arxiv.org/abs/1505.07427
     project page: http://mi.eng.cam.ac.uk/projects/relocalisation/#results
     github: https://github.com/alexgkendall/caffe-posenet
     github(TensorFlow): https://github.com/kentsommer/tensorflow-posenet
    
Modelling Uncertainty in Deep Learning for Camera Relocalization
    
     paper: http://arxiv.org/abs/1509.05909
    
Random Forests versus Neural Networks - What's Best for Camera Relocalization?
    
     arxiv: http://arxiv.org/abs/1609.05797
    
Deep Convolutional Neural Network for 6-DOF Image Localization
    
     arxiv: https://arxiv.org/abs/1611.02776
    
Image-based Localization with Spatial LSTMs
    
     arxiv: https://arxiv.org/abs/1611.07890
    
VidLoc: 6-DoF Video-Clip Relocalization
    
     arxiv: https://arxiv.org/abs/1702.06521
    
Towards CNN Map Compression for camera relocalisation
    
     arxiv: https://www.arxiv.org/abs/1703.00845
    
Camera Relocalization by Computing Pairwise Relative Poses Using Convolutional Neural Network
    
     intro: Aalto University & Indian Institute of Technology
     arxiv: https://arxiv.org/abs/1707.09733
    

      Counting Objects
Towards perspective-free object counting with deep learning
    
     intro: ECCV 2016. Counting CNN and Hydra CNN
     paper: http://agamenon.tsc.uah.es/Investigacion/gram/publications/eccv2016-onoro.pdf
     github: https://github.com/gramuah/ccnn
     poster: http://www.eccv2016.org/files/posters/P-3B-26.pdf
    
Using Convolutional Neural Networks to Count Palm Trees in Satellite Images
    
     arxiv: https://arxiv.org/abs/1701.06462
    
Count-ception: Counting by Fully Convolutional Redundant Counting
https://arxiv.org/abs/1703.08710
Counting Objects with Faster R-CNN
    
     blog: https://softwaremill.com/counting-objects-with-faster-rcnn/
     github: https://github.com/softberries/keras-frcnn
    
Drone-based Object Counting by Spatially Regularized Regional Proposal Network
https://arxiv.org/abs/1707.05972
FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras
    
     intro: ICCV 2017. CMU & Universidade de Lisboa
     arxiv: https://arxiv.org/abs/1707.09476
    
Representation Learning by Learning to Count
    
     intro: ICCV 2017 oral
     arxiv: https://arxiv.org/abs/1708.06734
    
Leaf Counting with Deep Convolutional and Deconvolutional Networks
    
     intro: ICCV 2017 Workshop on Computer Vision Problems in Plant Phenotyping
     arxiv: https://arxiv.org/abs/1708.07570
    

      Crowd Counting / Crowd Analysis
Large scale crowd analysis based on convolutional neural network
    
     paper: http://www.sciencedirect.com/science/article/pii/S0031320315001259
    
Deep People Counting in Extremely Dense Crowds
    
     intro: ACM 2015
     paper: http://yangliang.github.io/pdf/sp055u.pdf
    
Crossing-line Crowd Counting with Two-phase Deep Neural Networks
    
     intro: ECCV 2016
     paper: http://www.ee.cuhk.edu.hk/~rzhao/project/crossline_eccv16/ZhaoLZWeccv16.pdf
     poster: http://www.eccv2016.org/files/posters/P-3C-41.pdf
    
Cross-scene Crowd Counting via Deep Convolutional Neural Networks
    
     intro: CVPR 2015
     paper: http://www.ee.cuhk.edu.hk/~xgwang/papers/zhangLWYcvpr15.pdf
    
Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
    
     intro: CVPR 2016
     paper: http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Zhang_Single-Image_Crowd_Counting_CVPR_2016_paper.pdf
     paper: http://sist.shanghaitech.edu.cn/office/research/news/CVPR2016/paper/Single-Image%20Crowd%20Counting%20via%20Multi-Column%20Convolutional%20Neural%20Network.pdf
     dataset(pwd: p1rv): http://pan.baidu.com/s/1gfyNBTh
     slides: http://smartdsp.xmu.edu.cn/%E6%B1%87%E6%8A%A5pdf/crowd%20counting%E6%9E%97%E8%B4%A8%E9%94%90.pdf
    
CrowdNet: A Deep Convolutional Network for Dense Crowd Counting
    
     intro: ACM Multimedia (MM) 2016
     arxiv: http://arxiv.org/abs/1608.06197
    
Crowd Counting by Adapting Convolutional Neural Networks with Side Information
    
     arxiv: https://arxiv.org/abs/1611.06748
    
Fully Convolutional Crowd Counting On Highly Congested Scenes
    
     intro: VISAPP 2017
     arxiv: https://arxiv.org/abs/1612.00220
    
Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction
    
     intro: AAAI 2017
     project page: https://www.microsoft.com/en-us/research/publication/deep-spatio-temporal-residual-networks-for-citywide-crowd-flows-prediction/
     paper: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/ST-ResNet-AAAI17-Zhang.pdf
     github: https://github.com/lucktroy/DeepST/tree/master/scripts/papers/AAAI17
     ppt: https://www.microsoft.com/en-us/research/wp-content/uploads/2016/11/DeepST-crowd-prediction.pptx
     system: http://urbanflow.sigkdd.com.cn/
    
Multi-scale Convolutional Neural Networks for Crowd Counting
    
     arxiv: https://arxiv.org/abs/1702.02359
    
Mixture of Counting CNNs: Adaptive Integration of CNNs Specialized to Specific Appearance for Crowd Counting
https://arxiv.org/abs/1703.09393
Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks - Counting, Detection, and Tracking
https://arxiv.org/abs/1705.10118
ResnetCrowd: A Residual Deep Learning Architecture for Crowd Counting, Violent Behaviour Detection and Crowd Density Level Classification
    
     intro: AVSS 2017
     arxiv: https://arxiv.org/abs/1705.10698
    
Image Crowd Counting Using Convolutional Neural Network and Markov Random Field
    
     intro: CVPR 2017
     arxiv: https://arxiv.org/abs/1706.03725
    
A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation
https://arxiv.org/abs/1707.01202
Spatiotemporal Modeling for Crowd Counting in Videos
    
     intro: ICCV 2017
     arxiv: https://arxiv.org/abs/1707.07890
    
CNN-based Cascaded Multi-task Learning of High-level Prior and Density Estimation for Crowd Counting
    
     intro: AVSS 2017 (14th International Conference on Advanced Video and Signal Based Surveillance)
     arxiv: https://arxiv.org/abs/1707.09605
    
Switching Convolutional Neural Network for Crowd Counting
    
     intro: CVPR 2017. Indian Institute of Science
     project page: http://val.serc.iisc.ernet.in/crowdcnn/
     arxiv: https://arxiv.org/abs/1708.00199
     github: https://github.com/val-iisc/crowd-counting-scnn
    
Generating High-Quality Crowd Density Maps using Contextual Pyramid CNNs
    
     intro: ICCV 2017
     arxiv: https://arxiv.org/abs/1708.00953
    
Deep Spatial Regression Model for Image Crowd Counting
https://arxiv.org/abs/1710.09757
Crowd counting via scale-adaptive convolutional neural network
    
     intro: Tencent Youtu Lab
     arxiv: https://arxiv.org/abs/1711.04433
    

      Activity Recognition
Implementing a CNN for Human Activity Recognition in Tensorflow
    
     blog: http://aqibsaeed.github.io/2016-11-04-human-activity-recognition-cnn/
     github: https://github.com/aqibsaeed/Human-Activity-Recognition-using-CNN
    
Concurrent Activity Recognition with Multimodal CNN-LSTM Structure
    
     arxiv: https://arxiv.org/abs/1702.01638
    
CERN: Confidence-Energy Recurrent Network for Group Activity Recognition
    
     intro: CVPR 2017
     arxiv: https://arxiv.org/abs/1704.03058
    
Deploying Tensorflow model on Andorid device for Human Activity Recognition
    
     blog: http://aqibsaeed.github.io/2017-05-02-deploying-tensorflow-model-andorid-device-human-activity-recognition/
     github: https://github.com/aqibsaeed/Human-Activity-Recognition-using-CNN/tree/master/ActivityRecognition
    

      Music Classification / Sound Classification
Explaining Deep Convolutional Neural Networks on Music Classification
    
     arxiv: http://arxiv.org/abs/1607.02444
     blog: https://keunwoochoi.wordpress.com/2015/12/09/ismir-2015-lbd-auralisation-of-deep-convolutional-neural-networks-listening-to-learned-features-auralization/
     blog: https://keunwoochoi.wordpress.com/2016/03/23/what-cnns-see-when-cnns-see-spectrograms/
     github: https://github.com/keunwoochoi/Auralisation
     audio samples: https://soundcloud.com/kchoi-research
    
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
    
     project page: http://www.stat.ucla.edu/~yang.lu/project/deepFrame/main.html
     arxiv: http://arxiv.org/abs/1608.04363
    
Convolutional Recurrent Neural Networks for Music Classification

    
     arxiv: http://arxiv.org/abs/1609.04243
     blog: https://keunwoochoi.wordpress.com/2016/09/15/paper-is-out-convolutional-recurrent-neural-networks-for-music-classification/
     github: https://github.com/keunwoochoi/music-auto_tagging-keras
    
CNN Architectures for Large-Scale Audio Classification
    
     intro: Google
     arxiv: https://arxiv.org/abs/1609.09430
     demo: https://www.youtube.com/watch?v=oAAo_r7ZT8U&feature=youtu.be
    
SoundNet: Learning Sound Representations from Unlabeled Video
    
     intro: MIT. NIPS 2016
     project page: http://projects.csail.mit.edu/soundnet/
     arxiv: https://arxiv.org/abs/1610.09001
     paper: http://web.mit.edu/vondrick/soundnet.pdf
     github: https://github.com/cvondrick/soundnet
     github: https://github.com/eborboihuc/SoundNet-tensorflow
     youtube: https://www.youtube.com/watch?v=yJCjVvIY4dU
    
Deep Learning 'ahem' detector
    
     github: https://github.com/worldofpiggy/deeplearning-ahem-detector
     slides: https://docs.google.com/presentation/d/1QXQEOiAMj0uF2_Gafr2bn-kMniUJAIM1PLTFm1mUops/edit#slide=id.g35f391192_00
     mirror: https://pan.baidu.com/s/1c2KGlwO
    
GenreFromAudio: Finding the genre of a song with Deep Learning
    
     intro: A pipeline to build a dataset from your own music library and use it to fill the missing genres
     github: https://github.com/despoisj/DeepAudioClassification
    
TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition
    
     arxiv: https://arxiv.org/abs/1703.10667
     github: https://github.com/chihyaoma/Activity-Recognition-with-CNN-and-RNN
    
On the Robustness of Deep Convolutional Neural Networks for Music Classification
    
     intro: Queen Mary University of London & New York University
     arxiv: https://arxiv.org/abs/1706.02361
    

      NSFW Detection / Classification
Nipple Detection using Convolutional Neural Network
    
     reddit: https://www.reddit.com/over18?dest=https%3A%2F%2Fwww.reddit.com%2Fr%2FMachineLearning%2Fcomments%2F33n77s%2Fandroid_app_nipple_detection_using_convolutional%2F
    
Applying deep learning to classify pornographic images and videos
    
     arxiv: http://arxiv.org/abs/1511.08899
    
MODERATE, FILTER, OR CURATE ADULT CONTENT WITH CLARIFAI’S NSFW MODEL
    
     blog: http://blog.clarifai.com/moderate-filter-or-curate-adult-content-with-clarifais-nsfw-model/#.VzVhM-yECZY
    
WHAT CONVOLUTIONAL NEURAL NETWORKS LOOK AT WHEN THEY SEE NUDITY
    
     blog: http://blog.clarifai.com/what-convolutional-neural-networks-see-at-when-they-see-nudity#.VzVh_-yECZY
    
Open Sourcing a Deep Learning Solution for Detecting NSFW Images
    
     intro: Yahoo
     blog: https://yahooeng.tumblr.com/post/151148689421/open-sourcing-a-deep-learning-solution-for
     github: https://github.com/yahoo/open_nsfw
    
Miles Deep - AI Porn Video Editor
    
     intro: Deep Learning Porn Video Classifier/Editor with Caffe
     github: https://github.com/ryanjay0/miles-deep
    

      Image Reconstruction / Inpainting
Context Encoders: Feature Learning by Inpainting

    
     intro: CVPR 2016
     intro: Unsupervised Feature Learning by Image Inpainting using GANs
     project page: http://www.cs.berkeley.edu/~pathak/context_encoder/
     arxiv: https://arxiv.org/abs/1604.07379
     github(official): https://github.com/pathak22/context-encoder
     github: https://github.com/BoyuanJiang/context_encoder_pytorch
    
Semantic Image Inpainting with Perceptual and Contextual Losses
Semantic Image Inpainting with Deep Generative Models
    
     keywords: Deep Convolutional Generative Adversarial Network (DCGAN)
     arxiv: http://arxiv.org/abs/1607.07539
     github: https://github.com/bamos/dcgan-completion.tensorflow
    
High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
    
     intro: University of Southern California & Adobe Research
     arxiv: https://arxiv.org/abs/1611.09969
    
Face Image Reconstruction from Deep Templates
https://www.arxiv.org/abs/1703.00832
Deep Learning-Guided Image Reconstruction from Incomplete Data
https://arxiv.org/abs/1709.00584
Learning to Inpaint for Image Compression
https://arxiv.org/abs/1709.08855

      Image Restoration
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
    
     intro: NIPS 2016
     arxiv: http://arxiv.org/abs/1603.09056
    
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
    
     arxiv: http://arxiv.org/abs/1606.08921
    
Image Completion with Deep Learning in TensorFlow
    
     blog: http://bamos.github.io/2016/08/09/deep-completion/
    
Deeply Aggregated Alternating Minimization for Image Restoration
    
     arxiv: https://arxiv.org/abs/1612.06508
    
A New Convolutional Network-in-Network Structure and Its Applications in Skin Detection, Semantic Segmentation, and Artifact Reduction
    
     intro: Seoul National University
     arxiv: https://arxiv.org/abs/1701.06190
    
Generative Face Completion
    
     intro: CVPR 2017
     arxiv: https://arxiv.org/abs/1704.05838
    
MemNet: A Persistent Memory Network for Image Restoration
    
     intro: ICCV 2017 (Spotlight presentation)
     arxiv: https://arxiv.org/abs/1708.02209
     github: https://github.com/tyshiwo/MemNet
    
Deep Mean-Shift Priors for Image Restoration
    
     intro: NIPS 2017
     arxiv: https://arxiv.org/abs/1709.03749
    
xUnit: Learning a Spatial Activation Function for Efficient Image Restoration
https://arxiv.org/abs/1711.06445

      Image Super-Resolution
Super-Resolution.Benckmark
    
     intro: Benchmark and resources for single super-resolution algorithms
     github: https://github.com/huangzehao/Super-Resolution.Benckmark
    
Image Super-Resolution Using Deep Convolutional Networks
    
     intro: Microsoft Research
     project page: http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html
     arxiv: http://arxiv.org/abs/1501.00092
     training code: http://mmlab.ie.cuhk.edu.hk/projects/SRCNN/SRCNN_train.zip
     test code: http://mmlab.ie.cuhk.edu.hk/projects/SRCNN/SRCNN_v1.zip
     github(Keras): https://github.com/titu1994/Image-Super-Resolution
    
Learning a Deep Convolutional Network for Image Super-Resolution
    
     Baidu-pan: http://pan.baidu.com/s/1c0k0wRu
    
Shepard Convolutional Neural Networks
    
     paper: https://papers.nips.cc/paper/5774-shepard-convolutional-neural-networks.pdf
     github: https://github.com/jimmy-ren/vcnn_double-bladed/tree/master/applications/Shepard_CNN
    
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution
    
     intro: NIPS 2015
     paper: https://papers.nips.cc/paper/5778-bidirectional-recurrent-convolutional-networks-for-multi-frame-super-resolution
    
Deeply-Recursive Convolutional Network for Image Super-Resolution
    
     intro: CVPR 2016
     arxiv: http://arxiv.org/abs/1511.04491
     paper: http://cv.snu.ac.kr/publication/conf/2016/DRCN_CVPR2016.pdf
    
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
    
     intro: CVPR 2016 Oral
     project page: http://cv.snu.ac.kr/research/VDSR/
     arxiv: http://arxiv.org/abs/1511.04587
     code: http://cv.snu.ac.kr/research/VDSR/VDSR_code.zip
     github: https://github.com/huangzehao/caffe-vdsr
     github(Torch): https://github.com/pby5/vdsr_torch
    
Super-Resolution with Deep Convolutional Sufficient Statistics
    
     arxiv: http://arxiv.org/abs/1511.05666
    
Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network
    
     arxiv: http://arxiv.org/abs/1607.01977
    
Local- and Holistic- Structure Preserving Image Super Resolution via Deep Joint Component Learning
    
     arxiv: http://arxiv.org/abs/1607.07220
    
End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks
    
     arxiv: http://arxiv.org/abs/1607.07680
    
Accelerating the Super-Resolution Convolutional Neural Network
    
     intro: speed up of more than 40 times with even superior restoration quality, real-time performance on a generic CPU
     project page: http://mmlab.ie.cuhk.edu.hk/projects/FSRCNN.html
     arxiv: http://arxiv.org/abs/1608.00367
    
srez: Image super-resolution through deep learning
    
     github: https://github.com/david-gpu/srez
    
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
    
     arxiv: https://arxiv.org/abs/1609.04802
     github(Torch): https://github.com/leehomyc/Photo-Realistic-Super-Resoluton
     github: https://github.com/junhocho/SRGAN
     github(Keras): https://github.com/titu1994/Super-Resolution-using-Generative-Adversarial-Networks
     github: https://github.com/buriburisuri/SRGAN
    
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
    
     intro: CVPR 2016
     arxiv: http://arxiv.org/abs/1609.05158
     github: https://github.com/Tetrachrome/subpixel
    
Is the deconvolution layer the same as a convolutional layer?
    
     intro: A note on RealTime Single Image and Video SuperResolution Using an Efficient SubPixel Convolutional Neural Network.
     arxiv: http://arxiv.org/abs/1609.07009
    
Amortised MAP Inference for Image Super-resolution
    
     arxiv: https://arxiv.org/abs/1610.04490
    
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
    
     arxiv: https://arxiv.org/abs/1611.05250
    
Super-Resolution on Satellite Imagery using Deep Learning
    
     part 1: https://medium.com/the-downlinq/super-resolution-on-satellite-imagery-using-deep-learning-part-1-ec5c5cd3cd2#.4oxn9pafu
    
Neural Enhance: Super Resolution for images using deep learning.
    
     github: https://github.com/alexjc/neural-enhance
     docker: https://github.com/alexjc/neural-enhance/blob/master/docker-cpu.df
    
Texture Enhancement via High-Resolution Style Transfer for Single-Image Super-Resolution
    
     intro: Digital Media & Communications R&D Center, Samsung Electronics, Seoul, Korea
     arxiv: https://arxiv.org/abs/1612.00085
    
EnhanceNet: Single Image Super-Resolution through Automated Texture Synthesis
    
     arxiv: https://arxiv.org/abs/1612.07919
    
Learning a Mixture of Deep Networks for Single Image Super-Resolution
    
     project page: http://www.ifp.illinois.edu/~dingliu2/accv2016/
     arxiv: https://arxiv.org/abs/1701.00823
     code: http://www.ifp.illinois.edu/~dingliu2/accv2016/codes/python_accv2016.zip
    
Dual Recovery Network with Online Compensation for Image Super-Resolution
    
     arxiv: https://arxiv.org/abs/1701.05652
    
Super-resolution Using Constrained Deep Texture Synthesis
    
     intro: Brown University & Georgia Institute of Technology
     arxiv: https://arxiv.org/abs/1701.07604
    
Pixel Recursive Super Resolution
    
     arxiv: https://arxiv.org/abs/1702.00783
     github(Tensorflow): https://github.com/nilboy/pixel-recursive-super-resolution
    
GUN: Gradual Upsampling Network for single image super-resolution
    
     arxiv: https://arxiv.org/abs/1703.04244
    
Single Image Super-resolution with a Parameter Economic Residual-like Convolutional Neural Network
    
     intro: Extentions of mmm 2017 paper
     arxiv: https://arxiv.org/abs/1703.08173
    
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
    
     intro: CVPR 2017
     project page(code+dataset): http://vllab1.ucmerced.edu/~wlai24/LapSRN/
     arxiv: https://arxiv.org/abs/1704.03915
     github(Matlab+MatConvNet): https://github.com/phoenix104104/LapSRN
    
Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks
    
     project page: http://vllab.ucmerced.edu/wlai24/LapSRN/
     arxiv: https://arxiv.org/abs/1710.01992
     github: https://github.com/phoenix104104/LapSRN
    
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network
    
     intro: South China University of Technology
     arxiv: https://arxiv.org/abs/1705.05084
    
Super-Resolution via Deep Learning
    
     intro: COMSATS Institute of IT (CIIT)
     arxiv: https://arxiv.org/abs/1706.09077
    
High-Quality Face Image SR Using Conditional Generative Adversarial Networks
https://arxiv.org/abs/1707.00737
Enhanced Deep Residual Networks for Single Image Super-Resolution
    
     intro: CVPR 2017 workshop. Best paper award of the NTIRE2017 workshop, and the winners of the NTIRE2017 Challenge on Single Image Super-Resolution
     arxiv: https://arxiv.org/abs/1707.02921
     paper: http://cv.snu.ac.kr/publication/conf/2017/EDSR_fixed.pdf
     github: https://github.com/LimBee/NTIRE2017
    
Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network
    
     arxiv: https://arxiv.org/abs/1707.05425
     github(Tensorflow): https://github.com/jiny2001/dcscn-super-resolution
    
Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception Module
    
     intro: ICIP 2017
     arxiv: https://arxiv.org/abs/1707.07128
    
Attention-Aware Face Hallucination via Deep Reinforcement Learning
https://arxiv.org/abs/1708.03132
CISRDCNN: Super-resolution of compressed images using deep convolutional neural networks
https://arxiv.org/abs/1709.06229
Deep Inception-Residual Laplacian Pyramid Networks for Accurate Single Image Super-Resolution
    
     intro: Chongqing University
     arxiv: https://arxiv.org/abs/1711.05431
    
D-PCN: Parallel Convolutional Neural Networks for Image Recognition in Reverse Adversarial Style
{https://arxiv.org/abs/1711.04237}(https://arxiv.org/abs/1711.04237)
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution
    
     intro: IEEE Winter Conf. on Applications of Computer Vision (WACV) 2018, Lake Tahoe, USA
     arxiv: https://arxiv.org/abs/1711.04048
    

      Video Super-resolution
Detail-revealing Deep Video Super-resolution
    
     arxiv: https://arxiv.org/abs/1704.02738
     github: https://github.com/jiangsutx/SPMC_VideoSR
    
End-to-End Learning of Video Super-Resolution with Motion Compensation
    
     intro: GCPR 2017
     arxiv: https://arxiv.org/abs/1707.00471
    

      Image Denoising
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
    
     arxiv: http://arxiv.org/abs/1608.03981
     github: https://github.com/cszn/DnCNN
    
Medical image denoising using convolutional denoising autoencoders
    
     arxiv: http://arxiv.org/abs/1608.04667
    
Rectifier Neural Network with a Dual-Pathway Architecture for Image Denoising
    
     arxiv: http://arxiv.org/abs/1609.03024
    
Non-Local Color Image Denoising with Convolutional Neural Networks
    
     arxiv: https://arxiv.org/abs/1611.06757
    
Joint Visual Denoising and Classification using Deep Learning
    
     intro: ICIP 2016
     arxiv: https://arxiv.org/abs/1612.01075
     github: https://github.com/ganggit/jointmodel
    
Deep Convolutional Denoising of Low-Light Images
    
     arxiv: https://arxiv.org/abs/1701.01687
    
Deep Class Aware Denoising
    
     arxiv: https://arxiv.org/abs/1701.01698
    
End-to-End Learning for Structured Prediction Energy Networks
    
     intro: University of Massachusetts & CMU
     arxiv: https://arxiv.org/abs/1703.05667
    
Block-Matching Convolutional Neural Network for Image Denoising
https://arxiv.org/abs/1704.00524
When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach
https://arxiv.org/abs/1706.04284
Wide Inference Network for Image Denoising
https://arxiv.org/abs/1707.05414
Learning Pixel-Distribution Prior with Wider Convolution for Image Denoising
    
     arxiv: https://arxiv.org/abs/1707.09135
     github(MatConvNet): https://github.com/cswin/WIN
    
Image Denoising via CNNs: An Adversarial Approach
    
     intro: Indian Institute of Science
     arxiv: https://arxiv.org/abs/1708.00159
    
An ELU Network with Total Variation for Image Denoising
    
     intro: 24th International Conference on Neural Information Processing (2017)
     arxiv: https://arxiv.org/abs/1708.04317
    
Dilated Residual Network for Image Denoising
https://arxiv.org/abs/1708.05473
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
    
     arxiv: https://arxiv.org/abs/1710.04026
     github(MatConvNet): https://github.com/cszn/FFDNet
    
Universal Denoising Networks : A Novel CNN-based Network Architecture for Image Denoising
https://arxiv.org/abs/1711.07807

      Image Haze Removal
DehazeNet: An End-to-End System for Single Image Haze Removal
    
     arxiv: http://arxiv.org/abs/1601.07661
    
An All-in-One Network for Dehazing and Beyond
    
     intro: All-in-One Dehazing Network (AOD-Net)
     arxiv: https://arxiv.org/abs/1707.06543
    
Joint Transmission Map Estimation and Dehazing using Deep Networks
https://arxiv.org/abs/1708.00581
End-to-End United Video Dehazing and Detection
https://arxiv.org/abs/1709.03919
Image Dehazing using Bilinear Composition Loss Function
https://arxiv.org/abs/1710.00279
Learning Aggregated Transmission Propagation Networks for Haze Removal and Beyond
https://arxiv.org/abs/1711.06787

      Image Rain Removal / De-raining
Clearing the Skies: A deep network architecture for single-image rain removal
    
     intro: DerainNet
     project page: http://smartdsp.xmu.edu.cn/derainNet.html
     arxiv: http://arxiv.org/abs/1609.02087
     code(Matlab): http://smartdsp.xmu.edu.cn/memberpdf/fuxueyang/derainNet/code.zip
    
Joint Rain Detection and Removal via Iterative Region Dependent Multi-Task Learning
    
     arxiv: http://arxiv.org/abs/1609.07769
    
Image De-raining Using a Conditional Generative Adversarial Network
    
     arxiv: https://arxiv.org/abs/1701.05957
    

      Fence Removal
Deep learning based fence segmentation and removal from an image using a video sequence
    
     intro: ECCV Workshop on Video Segmentation, 2016
     arxiv: http://arxiv.org/abs/1609.07727
    

      Snow Removal
DesnowNet: Context-Aware Deep Network for Snow Removal
https://arxiv.org/abs/1708.04512

      Blur Detection and Removal
Learning to Deblur
    
     arxiv: http://arxiv.org/abs/1406.7444
    
Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal
    
     arxiv: http://arxiv.org/abs/1503.00593
    
End-to-End Learning for Image Burst Deblurring
    
     arxiv: http://arxiv.org/abs/1607.04433
    
Deep Video Deblurring
    
     intro: CVPR 2017 spotlight paper
     project page(code+dataset): http://www.cs.ubc.ca/labs/imager/tr/2017/DeepVideoDeblurring/
     arxiv: https://arxiv.org/abs/1611.08387 https://github.com/shuochsu/DeepVideoDeblurring
    
Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring
    
     arxiv: https://arxiv.org/abs/1612.02177
     github(official. Torch)): https://github.com/SeungjunNah/DeepDeblur_release
    
From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur
    
     arxiv: https://arxiv.org/abs/1612.02583
    
Motion Deblurring in the Wild
    
     arxiv: https://arxiv.org/abs/1701.01486
    
Deep Face Deblurring
https://arxiv.org/abs/1704.08772
Learning Blind Motion Deblurring
    
     intro: ICCV 2017
     arxiv: https://arxiv.org/abs/1708.04208
    
Deep Generative Filter for Motion Deblurring
https://arxiv.org/abs/1709.03481
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
    
     arxiv: https://arxiv.org/abs/1711.07064
     github: https://github.com/KupynOrest/DeblurGAN
    

      Image Compression
An image compression and encryption scheme based on deep learning
    
     arxiv: http://arxiv.org/abs/1608.05001
    
Full Resolution Image Compression with Recurrent Neural Networks
    
     arxiv: http://arxiv.org/abs/1608.05148
     github: https://github.com/tensorflow/models/tree/master/compression
    
Image Compression with Neural Networks
    
     blog: https://research.googleblog.com/2016/09/image-compression-with-neural-networks.html
    
Lossy Image Compression With Compressive Autoencoders
    
     paper: http://openreview.net/pdf?id=rJiNwv9gg
     review: http://qz.com/835569/twitter-is-getting-close-to-making-all-your-pictures-just-a-little-bit-smaller/
    
End-to-end Optimized Image Compression
    
     arxiv: https://arxiv.org/abs/1611.01704
     notes: https://blog.acolyer.org/2017/05/08/end-to-end-optimized-image-compression/
    
CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression
    
     arxiv: https://arxiv.org/abs/1611.07233
    
Semantic Perceptual Image Compression using Deep Convolution Networks
    
     intro: Accepted to Data Compression Conference
     intro: Semantic JPEG image compression using deep convolutional neural network (CNN)
     arxiv: https://arxiv.org/abs/1612.08712
     github: https://github.com/iamaaditya/image-compression-cnn
    
Generative Compression
    
     intro: MIT
     arxiv: https://arxiv.org/abs/1703.01467
    
Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
https://arxiv.org/abs/1703.10114
Learning Convolutional Networks for Content-weighted Image Compression
https://arxiv.org/abs/1703.10553
Real-Time Adaptive Image Compression
    
     intro: ICML 2017
     keywords: GAN
     project page: http://www.wave.one/icml2017
     arxiv: https://arxiv.org/abs/1705.05823
    

      Image Quality Assessment
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
    
     arxiv: https://arxiv.org/abs/1612.01697
    

      Image Matting
Deep Image Matting
    
     intro: CVPR 2017
     intro: Beckman Institute for Advanced Science and Technology & Adobe Research
     arxiv: https://arxiv.org/abs/1703.03872
    
Fast Deep Matting for Portrait Animation on Mobile Phone
    
     intro: ACM Multimedia Conference (MM) 2017
     intro: does not need any interaction and can realize real-time matting with 15 fps
     arxiv: https://arxiv.org/abs/1707.08289
    

      Image Blending
GP-GAN: Towards Realistic High-Resolution Image Blending
    
     project page: https://wuhuikai.github.io/GP-GAN-Project/
     arxiv: https://arxiv.org/abs/1703.07195
     github(Official, Chainer): https://github.com/wuhuikai/GP-GAN
    

      Image Enhancement
Deep Bilateral Learning for Real-Time Image Enhancement
    
     intro: MIT & Google Research
     arxiv: https://arxiv.org/abs/1707.02880
    
Aesthetic-Driven Image Enhancement by Adversarial Learning
    
     intro: CUHK
     arxiv: https://arxiv.org/abs/1707.05251
    

      Abnormality Detection / Anomaly Detection
Toward a Taxonomy and Computational Models of Abnormalities in Images
    
     arxiv: http://arxiv.org/abs/1512.01325
    

      Depth Prediction / Depth Estimation
Deep Convolutional Neural Fields for Depth Estimation from a Single Image
    
     intro: CVPR 2015
     arxiv: https://arxiv.org/abs/1411.6387
    
Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
    
     intro: IEEE T. Pattern Analysis and Machine Intelligence
     arxiv: https://arxiv.org/abs/1502.07411
     bitbucket: https://bitbucket.org/fayao/dcnf-fcsp
    
Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
    
     intro: ECCV 2016
     arxiv: https://arxiv.org/abs/1603.04992
     github: https://github.com/Ravi-Garg/Unsupervised_Depth_Estimation
    
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions
    
     intro: NIPS 2016
     project pag: http://ttic.uchicago.edu/~ayanc/mdepth/
     arxiv: http://arxiv.org/abs/1605.07081
     github: https://github.com/ayanc/mdepth/
    
Deeper Depth Prediction with Fully Convolutional Residual Networks
    
     arxiv: https://arxiv.org/abs/1606.00373
     github: https://github.com/iro-cp/FCRN-DepthPrediction
    
Single image depth estimation by dilated deep residual convolutional neural network and soft-weight-sum inference
https://arxiv.org/abs/1705.00534
Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and Soft-Weighted-Sum Inference
    
     intro: Northwestern Polytechnical University
     arxiv: https://arxiv.org/abs/1708.02287
    
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
    
     arxiv: https://arxiv.org/abs/1709.07492
     video: https://www.youtube.com/watch?v=vNIIT_M7x7Y
     github: https://github.com/fangchangma/sparse-to-dense
    

      Texture Synthesis
Texture Synthesis Using Convolutional Neural Networks
    
     arxiv: http://arxiv.org/abs/1505.07376
    
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
    
     intro: IMCL 2016
     arxiv: http://arxiv.org/abs/1603.03417
     github: https://github.com/DmitryUlyanov/texture_nets
     notes: https://blog.acolyer.org/2016/09/23/texture-networks-feed-forward-synthesis-of-textures-and-stylized-images/
    
Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
    
     arxiv: http://arxiv.org/abs/1604.04382
     github(Torch): https://github.com/chuanli11/MGANs
    
Texture Synthesis with Spatial Generative Adversarial Networks
    
     arxiv: https://arxiv.org/abs/1611.08207
    
Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis
    
     intro: Skolkovo Institute of Science and Technology & Yandex & University of Oxford
     arxiv: https://arxiv.org/abs/1701.02096
    
Deep TEN: Texture Encoding Network
    
     intro: CVPR 2017
     project page: http://zhanghang1989.github.io/DeepEncoding/
     arxiv: https://arxiv.org/abs/1612.02844
     github: https://github.com/zhanghang1989/Deep-Encoding
     notes: https://zhuanlan.zhihu.com/p/25013378
    
Diversified Texture Synthesis with Feed-forward Networks
    
     intro: CVPR 2017. University of California & Adobe Research
     arxiv: https://arxiv.org/abs/1703.01664
     github: https://github.com/Yijunmaverick/MultiTextureSynthesis
    

      Image Cropping
A2-RL: Aesthetics Aware Reinforcement Learning for Automatic Image Cropping
https://arxiv.org/abs/1709.04595
Deep Cropping via Attention Box Prediction and Aesthetics Assessment
    
     intro: ICCV 2017
     arxiv: https://arxiv.org/abs/1710.08014
    

      Image Synthesis
Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
    
     arxiv: http://arxiv.org/abs/1601.04589
    
Generative Adversarial Text to Image Synthesis

    
     intro: ICML 2016
     arxiv: http://arxiv.org/abs/1605.05396
     github(Tensorflow): https://github.com/paarthneekhara/text-to-image
    
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
    
     intro: Rutgers University & Lehigh University & The Chinese University of Hong Kong & University of North Carolina at Charlotte
     arxiv: https://arxiv.org/abs/1612.03242
     github: https://github.com/hanzhanggit/StackGAN
     github: https://github.com/brangerbriz/docker-StackGAN
    

      Image Tagging
Fast Zero-Shot Image Tagging

    
     project: http://crcv.ucf.edu/projects/fastzeroshot/
    
Flexible Image Tagging with Fast0Tag

    
     blog: https://gab41.lab41.org/flexible-image-tagging-with-fast0tag-681c6283c9b7
    
Sampled Image Tagging and Retrieval Methods on User Generated Content
    
     arxiv: https://arxiv.org/abs/1611.06962
     github: https://github.com/lab41/attalos
    
Kill Two Birds with One Stone: Weakly-Supervised Neural Network for Image Annotation and Tag Refinement
    
     intro: AAAI 2018
     arxiv: https://arxiv.org/abs/1711.06998
    

      Image Matching
Learning Fine-grained Image Similarity with Deep Ranking
    
     intro: CVPR 2014
     intro: Triplet Sampling
     arxiv: http://arxiv.org/abs/1404.4661
    
Learning to compare image patches via convolutional neural networks
    
     intro: CVPR 2015. siamese network
     project page: http://imagine.enpc.fr/~zagoruys/deepcompare.html
     paper: http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Zagoruyko_Learning_to_Compare_2015_CVPR_paper.pdf
     github: https://github.com/szagoruyko/cvpr15deepcompare
    
MatchNet: Unifying Feature and Metric Learning for Patch-Based Matching
    
     intro: CVPR 2015. siamese network
     paper: http://www.cs.unc.edu/~xufeng/cs/papers/cvpr15-matchnet.pdf
     extended abstract: http://www.cv-foundation.org/openaccess/content_cvpr_2015/ext/2A_114_ext.pdf
     github: https://github.com/hanxf/matchnet
    
Fashion Style in 128 Floats

    
     intro: CVPR 2016. StyleNet
     project page: http://hi.cs.waseda.ac.jp/~esimo/en/research/stylenet/
     paper: http://hi.cs.waseda.ac.jp/~esimo/publications/SimoSerraCVPR2016.pdf
     github: https://github.com/bobbens/cvpr2016_stylenet
    
Fully-Trainable Deep Matching
    
     intro: BMVC 2016
     project page: http://lear.inrialpes.fr/src/deepmatching/
     arxiv: http://arxiv.org/abs/1609.03532
    
Local Similarity-Aware Deep Feature Embedding
    
     intro: NIPS 2016
     arxiv: https://arxiv.org/abs/1610.08904
    
Convolutional neural network architecture for geometric matching
    
     intro: CVPR 2017. Inria
     project page: http://www.di.ens.fr/willow/research/cnngeometric/
     arxiv: https://arxiv.org/abs/1703.05593
     github: https://github.com/ignacio-rocco/cnngeometric_matconvnet
    
Multi-Image Semantic Matching by Mining Consistent Features
https://arxiv.org/abs/1711.07641

      Image Editing
Neural Photo Editing with Introspective Adversarial Networks

    
     intro: Heriot-Watt University
     arxiv: http://arxiv.org/abs/1609.07093
     github: https://github.com/ajbrock/Neural-Photo-Editor
    
Deep Feature Interpolation for Image Content Changes
    
     intro: CVPR 2017. Cornell University & Washington University
     arxiv: https://arxiv.org/abs/1611.05507
     github(official): https://github.com/paulu/deepfeatinterp
     github: https://github.com/slang03/dfi-tensorflow
    
Invertible Conditional GANs for image editing

    
     intro: NIPS 2016 Workshop on Adversarial Training
     arxiv: https://arxiv.org/abs/1611.06355
     github: https://github.com/Guim3/IcGAN
    
Semantic Facial Expression Editing using Autoencoded Flow
    
     intro: University of Illinois at Urbana-Champaign & The Chinese University of Hong Kong & Google
     arxiv: https://arxiv.org/abs/1611.09961
    
Language-Based Image Editing with Recurrent Attentive Models
https://arxiv.org/abs/1711.06288

      Face Swap
Fast Face-swap Using Convolutional Neural Networks
    
     intro: Ghent University & Twitter
     arxiv: https://arxiv.org/abs/1611.09577
    

      Face Editing
Neural Face Editing with Intrinsic Image Disentangling
    
     intro: CVPR 2017 oral
     project page: http://www3.cs.stonybrook.edu/~cvl/content/neuralface/neuralface.html
     arxiv: https://arxiv.org/abs/1704.04131
    

      Deep Learning for Makeup
Makeup like a superstar: Deep Localized Makeup Transfer Network
    
     intro: IJCAI 2016
     arxiv: http://arxiv.org/abs/1604.07102
    
Makeup-Go: Blind Reversion of Portrait Edit
    
     intro: The Chinese University of Hong Kong & Tencent Youtu Lab
     paper: http://openaccess.thecvf.com/content_ICCV_2017/papers/Chen_Makeup-Go_Blind_Reversion_ICCV_2017_paper.pdf
     paper: http://open.youtu.qq.com/content/file/iccv17_makeupgo.pdf
    

      Music Tagging
Automatic tagging using deep convolutional neural networks
    
     arxiv: https://arxiv.org/abs/1606.00298
     github: https://github.com/keunwoochoi/music-auto_tagging-keras
    
Music tagging and feature extraction with MusicTaggerCRNN
https://keras.io/applications/#music-tagging-and-feature-extraction-with-musictaggercrnn

      Action Recognition
Single Image Action Recognition by Predicting Space-Time Saliency
https://arxiv.org/abs/1705.04641
Attentional Pooling for Action Recognition
    
     intro: NIPS 2017
     project page: https://rohitgirdhar.github.io/AttentionalPoolingAction/
     arxiv: https://arxiv.org/abs/1711.01467
     github: https://github.com/rohitgirdhar/AttentionalPoolingAction/
    

      CTR Prediction
Deep CTR Prediction in Display Advertising
    
     intro: ACM Multimedia Conference 2016
     arxiv: https://arxiv.org/abs/1609.06018
    
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
    
     intro: Harbin Institute of Technology & Huawei
     arxiv: https://arxiv.org/abs/1703.04247
    
Deep Interest Network for Click-Through Rate Prediction
    
     intro: Alibaba Inc.
     arxiv: https://arxiv.org/abs/1706.06978
    
Image Matters: Jointly Train Advertising CTR Model with Image Representation of Ad and User Behavior
    
     intro: Alibaba Inc.
     arxiv: https://arxiv.org/abs/1711.06505
    

      Cryptography
Learning to Protect Communications with Adversarial Neural Cryptography
    
     intro: Google Brain
     arxiv: https://arxiv.org/abs/1610.06918
     github(Theano): https://github.com/nlml/adversarial-neural-crypt
     github(TensorFlow): https://github.com/ankeshanand/neural-cryptography-tensorflow
    
Adversarial Neural Cryptography in Theano
    
     blog: https://nlml.github.io/neural-networks/adversarial-neural-cryptography/
    
Embedding Watermarks into Deep Neural Networks
    
     arxiv: https://arxiv.org/abs/1701.04082
     github: https://github.com/yu4u/dnn-watermark
    

      Cyber Security
Collection of Deep Learning Cyber Security Research Papers
    
     blog: https://medium.com/@jason_trost/collection-of-deep-learning-cyber-security-research-papers-e1f856f71042#.fcus2cu9m
    

      Lip Reading
LipNet: Sentence-level Lipreading
LipNet: End-to-End Sentence-level Lipreading
    
     arxiv: https://arxiv.org/abs/1611.01599
     paper: http://openreview.net/pdf?id=BkjLkSqxg
     github: https://github.com/bshillingford/LipNet
    
Lip Reading Sentences in the Wild
    
     intro: University of Oxford & Google DeepMind
     arxiv: https://arxiv.org/abs/1611.05358
     youtube: https://www.youtube.com/watch?v=5aogzAUPilE
    
Combining Residual Networks with LSTMs for Lipreading
    
     arxiv: https://arxiv.org/abs/1703.04105
    
End-to-End Multi-View Lipreading
    
     intro: BMVC 2017
     arxiv: https://arxiv.org/abs/1709.00443
    

      Event Recognition
Better Exploiting OS-CNNs for Better Event Recognition in Images
    
     arxiv: http://arxiv.org/abs/1510.03979
    
Transferring Object-Scene Convolutional Neural Networks for Event Recognition in Still Images
    
     arxiv: http://arxiv.org/abs/1609.00162
    
IOD-CNN: Integrating Object Detection Networks for Event Recognition
https://arxiv.org/abs/1703.07431

      Others
Selfai: Predicting Facial Beauty in Selfies
Selfai: A Method for Understanding Beauty in Selfies
    
     blog: http://www.erogol.com/selfai-predicting-facial-beauty-selfies/
     github: https://github.com/erogol/beauty.torch
    
Deep Learning Enables You to Hide Screen when Your Boss is Approaching
    
     blog: http://ahogrammer.com/2016/11/15/deep-learning-enables-you-to-hide-screen-when-your-boss-is-approaching/
     github: https://github.com/Hironsan/BossSensor
    

      Blogs
40 Ways Deep Learning is Eating the World
https://medium.com/intuitionmachine/the-ultimate-deep-learning-applications-list-434d1425da1d#.rxq8xvbfz
Applications
http://www.deeplearningpatterns.com/doku.php/applications
Systematic Approach To Applications Of Deep Learning
https://gettocode.com/2016/11/25/systematic-approach-to-applications-of-deep-learning/

      Resources
Deep Learning Gallery - a curated collection of deep learning projects
http://deeplearninggallery.com/

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