Applications

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/

Makeup like a superstar: Deep Localized Makeup Transfer Network

• intro: IJCAI 2016
• arxiv: http://arxiv.org/abs/1604.07102

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

Learning Two-Branch Neural Networks for Image-Text Matching Tasks

https://arxiv.org/abs/1704.03470

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

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

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

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

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

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

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

• keywords: Deep Convolutional Generative Adversarial Network (DCGAN)
• arxiv: http://arxiv.org/abs/1607.07539

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

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

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

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

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

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

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

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

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

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 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

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

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

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

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

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
• arxiv: https://arxiv.org/abs/1706.06978

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

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/