awesome-deep-vision
A curated list of deep learning resources for computer vision
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Awesome Deep Vision / Sharing | |||
http://twitter.com/home?status=http://jiwonkim.org/awesome-deep-vision%0ADeep | [Share on Twitter]( Learning Resources for Computer Vision) | ||
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Awesome Deep Vision / Papers / ImageNet Classification | |||
Paper | Microsoft (Deep Residual Learning) [ ][ ] | ||
[Paper] | Microsoft (PReLu/Weight Initialization) | ||
[Paper] | Batch Normalization | ||
[Paper] | GoogLeNet | ||
[Web] | VGG-Net | ||
[Paper] | AlexNet | ||
Awesome Deep Vision / Papers / Object Detection | |||
[Paper] | PVANET | ||
[Paper] | OverFeat, NYU | ||
[Paper-CVPR14] | R-CNN, UC Berkeley | ||
[Paper] | SPP, Microsoft Research | ||
[Paper] | Fast R-CNN, Microsoft Research | ||
[Paper] | Faster R-CNN, Microsoft Research | ||
[Paper] | R-CNN minus R, Oxford | ||
[Paper] | End-to-end people detection in crowded scenes | ||
[Paper] | You Only Look Once: Unified, Real-Time Object Detection , , , | ||
[Paper] | Inside-Outside Net | ||
[Paper] | Deep Residual Network (Current State-of-the-Art) | ||
Paper | Weakly Supervised Object Localization with Multi-fold Multiple Instance Learning [ ] | ||
[Paper] | R-FCN | ||
[Paper] | SSD | ||
[Paper] | Speed/accuracy trade-offs for modern convolutional object detectors | ||
Awesome Deep Vision / Papers / Video Classification | |||
Paper | Nicolas Ballas, Li Yao, Pal Chris, Aaron Courville, "Delving Deeper into Convolutional Networks for Learning Video Representations", ICLR 2016. [ ] | ||
Paper | Michael Mathieu, camille couprie, Yann Lecun, "Deep Multi Scale Video Prediction Beyond Mean Square Error", ICLR 2016. [ ] | ||
Awesome Deep Vision / Papers / Object Tracking | |||
[Paper] | Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han, Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network, arXiv:1502.06796 | ||
[Paper] | Hanxi Li, Yi Li and Fatih Porikli, DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking, BMVC, 2014 | ||
[Paper] | N Wang, DY Yeung, Learning a Deep Compact Image Representation for Visual Tracking, NIPS, 2013 | ||
Paper | Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang, Hierarchical Convolutional Features for Visual Tracking, ICCV 2015 [ ] [ ] | ||
Paper | Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu, Visual Tracking with fully Convolutional Networks, ICCV 2015 [ ] [ ] | ||
Paper | Hyeonseob Namand Bohyung Han, Learning Multi-Domain Convolutional Neural Networks for Visual Tracking, [ ] [ ] [ ] | ||
Awesome Deep Vision / Papers / Low-Level Vision / Iterative Image Reconstruction | |||
[Paper] | Sven Behnke: Learning Iterative Image Reconstruction. IJCAI, 2001 | ||
[Paper] | Sven Behnke: Learning Iterative Image Reconstruction in the Neural Abstraction Pyramid. International Journal of Computational Intelligence and Applications, vol. 1, no. 4, pp. 427-438, 2001 | ||
Awesome Deep Vision / Papers / Low-Level Vision | |||
[Web] | Super-Resolution (SRCNN) | ||
Awesome Deep Vision / Papers / Low-Level Vision / Very Deep Super-Resolution | |||
[Paper] | Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Accurate Image Super-Resolution Using Very Deep Convolutional Networks, arXiv:1511.04587, 2015 | ||
Awesome Deep Vision / Papers / Low-Level Vision / Deeply-Recursive Convolutional Network | |||
[Paper] | Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee, Deeply-Recursive Convolutional Network for Image Super-Resolution, arXiv:1511.04491, 2015 | ||
Awesome Deep Vision / Papers / Low-Level Vision / Casade-Sparse-Coding-Network | |||
[Paper] | Zhaowen Wang, Ding Liu, Wei Han, Jianchao Yang and Thomas S. Huang, Deep Networks for Image Super-Resolution with Sparse Prior. ICCV, 2015 | ||
Awesome Deep Vision / Papers / Low-Level Vision / Perceptual Losses for Super-Resolution | |||
[Paper] | Justin Johnson, Alexandre Alahi, Li Fei-Fei, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, arXiv:1603.08155, 2016 | ||
Awesome Deep Vision / Papers / Low-Level Vision / SRGAN | |||
[Paper] | Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi, Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network, arXiv:1609.04802v3, 2016 | ||
Awesome Deep Vision / Papers / Low-Level Vision / Others | |||
[Paper ICONIP-2014] | Osendorfer, Christian, Hubert Soyer, and Patrick van der Smagt, Image Super-Resolution with Fast Approximate Convolutional Sparse Coding, ICONIP, 2014 | ||
Awesome Deep Vision / Papers / Low-Level Vision | |||
[Paper] | Optical Flow (FlowNet) | ||
[Paper-arXiv15] | Compression Artifacts Reduction | ||
Awesome Deep Vision / Papers / Low-Level Vision / Blur Removal | |||
[Paper] | Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf, Learning to Deblur, arXiv:1406.7444 | ||
[Paper] | Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce, Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal, CVPR, 2015 | ||
Awesome Deep Vision / Papers / Low-Level Vision | |||
[Web] | Image Deconvolution | ||
[Paper] | Deep Edge-Aware Filter | ||
[Paper] | Computing the Stereo Matching Cost with a Convolutional Neural Network | ||
[Paper] | Colorful Image Colorization Richard Zhang, Phillip Isola, Alexei A. Efros, ECCV, 2016 , | ||
[Blog] | Ryan Dahl, | ||
[Paper] | Feature Learning by Inpainting | ||
Awesome Deep Vision / Papers / Edge Detection | |||
[Paper] | Holistically-Nested Edge Detection | ||
[Paper] | DeepEdge | ||
[Paper] | DeepContour | ||
Awesome Deep Vision / Papers / Semantic Segmentation | |||
leaderboards | PASCAL VOC2012 Challenge Leaderboard (01 Sep. 2016) (from PASCAL VOC2012 ) | ||
Awesome Deep Vision / Papers / Semantic Segmentation / SEC: Seed, Expand and Constrain | |||
[Paper] | Alexander Kolesnikov, Christoph Lampert, Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation, ECCV, 2016 | ||
Awesome Deep Vision / Papers / Semantic Segmentation / Adelaide | |||
[Paper] | Guosheng Lin, Chunhua Shen, Ian Reid, Anton van dan Hengel, Efficient piecewise training of deep structured models for semantic segmentation, arXiv:1504.01013. (1st ranked in VOC2012) | ||
[Paper] | Guosheng Lin, Chunhua Shen, Ian Reid, Anton van den Hengel, Deeply Learning the Messages in Message Passing Inference, arXiv:1508.02108. (4th ranked in VOC2012) | ||
Awesome Deep Vision / Papers / Semantic Segmentation / Deep Parsing Network (DPN) | |||
[Paper] | Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, Xiaoou Tang, Semantic Image Segmentation via Deep Parsing Network, arXiv:1509.02634 / ICCV 2015 (2nd ranked in VOC 2012) | ||
Awesome Deep Vision / Papers / Semantic Segmentation | |||
[Paper] | CentraleSuperBoundaries, INRIA | ||
[Paper] | BoxSup | ||
Awesome Deep Vision / Papers / Semantic Segmentation / POSTECH | |||
[Paper] | Hyeonwoo Noh, Seunghoon Hong, Bohyung Han, Learning Deconvolution Network for Semantic Segmentation, arXiv:1505.04366. (7th ranked in VOC2012) | ||
[Paper] | Seunghoon Hong, Hyeonwoo Noh, Bohyung Han, Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation, arXiv:1506.04924 | ||
Paper | Seunghoon Hong,Junhyuk Oh, Bohyung Han, and Honglak Lee, Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network, arXiv:1512.07928 [ ] [ ] | ||
Awesome Deep Vision / Papers / Semantic Segmentation | |||
[Paper] | Conditional Random Fields as Recurrent Neural Networks | ||
Awesome Deep Vision / Papers / Semantic Segmentation / DeepLab | |||
[Paper] | Liang-Chieh Chen, George Papandreou, Kevin Murphy, Alan L. Yuille, Weakly-and semi-supervised learning of a DCNN for semantic image segmentation, arXiv:1502.02734. (9th ranked in VOC2012) | ||
Awesome Deep Vision / Papers / Semantic Segmentation | |||
[Paper] | Zoom-out | ||
[Paper] | Joint Calibration | ||
[Paper-CVPR15] | Fully Convolutional Networks for Semantic Segmentation | ||
[Paper] | Hypercolumn | ||
Awesome Deep Vision / Papers / Semantic Segmentation / Deep Hierarchical Parsing | |||
[Paper] | Abhishek Sharma, Oncel Tuzel, David W. Jacobs, Deep Hierarchical Parsing for Semantic Segmentation, CVPR, 2015 | ||
Awesome Deep Vision / Papers / Semantic Segmentation | |||
[Paper-ICML12] | Learning Hierarchical Features for Scene Labeling | ||
[Web] | University of Cambridge | ||
Awesome Deep Vision / Papers / Semantic Segmentation / [Web] | |||
[Paper] | Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation." arXiv preprint arXiv:1511.00561, 2015 | ||
Awesome Deep Vision / Papers / Semantic Segmentation | |||
[Paper] | Alex Kendall, Vijay Badrinarayanan and Roberto Cipolla "Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding." arXiv preprint arXiv:1511.02680, 2015 | ||
Awesome Deep Vision / Papers / Semantic Segmentation / Princeton | |||
Paper | Fisher Yu, Vladlen Koltun, "Multi-Scale Context Aggregation by Dilated Convolutions", ICLR 2016, [ ] | ||
Awesome Deep Vision / Papers / Semantic Segmentation / Univ. of Washington, Allen AI | |||
Paper | Hamid Izadinia, Fereshteh Sadeghi, Santosh Kumar Divvala, Yejin Choi, Ali Farhadi, "Segment-Phrase Table for Semantic Segmentation, Visual Entailment and Paraphrasing", ICCV, 2015, [ ] | ||
Awesome Deep Vision / Papers / Semantic Segmentation / INRIA | |||
Paper | Iasonas Kokkinos, "Pusing the Boundaries of Boundary Detection Using deep Learning", ICLR 2016, [ ] | ||
Awesome Deep Vision / Papers / Semantic Segmentation / UCSB | |||
Paper | Niloufar Pourian, S. Karthikeyan, and B.S. Manjunath, "Weakly supervised graph based semantic segmentation by learning communities of image-parts", ICCV, 2015, [ ] | ||
Awesome Deep Vision / Papers / Visual Attention and Saliency | |||
[Paper] | Mr-CNN | ||
[Paper] | Learning a Sequential Search for Landmarks | ||
[Paper] | Multiple Object Recognition with Visual Attention | ||
[Paper] | Recurrent Models of Visual Attention | ||
Awesome Deep Vision / Papers / Object Recognition | |||
[Paper] | Weakly-supervised learning with convolutional neural networks | ||
[Paper] | FV-CNN | ||
Awesome Deep Vision / Papers / Understanding CNN | |||
[Paper] | Karel Lenc, Andrea Vedaldi, Understanding image representations by measuring their equivariance and equivalence, CVPR, 2015 | ||
[Paper] | Anh Nguyen, Jason Yosinski, Jeff Clune, Deep Neural Networks are Easily Fooled:High Confidence Predictions for Unrecognizable Images, CVPR, 2015 | ||
[Paper] | Aravindh Mahendran, Andrea Vedaldi, Understanding Deep Image Representations by Inverting Them, CVPR, 2015 | ||
[arXiv Paper] | Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba, Object Detectors Emerge in Deep Scene CNNs, ICLR, 2015 | ||
[Paper] | Alexey Dosovitskiy, Thomas Brox, Inverting Visual Representations with Convolutional Networks, arXiv, 2015 | ||
[Paper] | Matthrew Zeiler, Rob Fergus, Visualizing and Understanding Convolutional Networks, ECCV, 2014 | ||
Awesome Deep Vision / Papers / Image and Language | |||
[Paper] | UCLA / Baidu | ||
[Paper] | Toronto | ||
[Paper] | Berkeley | ||
[Paper] | |||
[Web] | Stanford | ||
[Paper] | UML / UT | ||
[Paper-arXiv] | CMU / Microsoft | ||
[Paper] | Microsoft | ||
Web | Univ. Montreal / Univ. Toronto [ ] [ ] | ||
Paper | Idiap / EPFL / Facebook [ ] | ||
Paper | UCLA / Baidu [ ] | ||
Awesome Deep Vision / Papers / Image and Language / MS + Berkeley | |||
Paper | Jacob Devlin, Saurabh Gupta, Ross Girshick, Margaret Mitchell, C. Lawrence Zitnick, Exploring Nearest Neighbor Approaches for Image Captioning, arXiv:1505.04467 [ ] | ||
Paper | Jacob Devlin, Hao Cheng, Hao Fang, Saurabh Gupta, Li Deng, Xiaodong He, Geoffrey Zweig, Margaret Mitchell, Language Models for Image Captioning: The Quirks and What Works, arXiv:1505.01809 [ ] | ||
Awesome Deep Vision / Papers / Image and Language | |||
Paper | Adelaide [ ] | ||
Paper | Tilburg [ ] | ||
Paper | Univ. Montreal [ ] | ||
Paper | Cornell [ ] | ||
Paper | MS + City Univ. of HongKong [ ] | ||
[Web] | Berkeley | ||
[Paper] | UT / UML / Berkeley | ||
[Paper] | Microsoft | ||
[Paper] | UT / Berkeley / UML | ||
Paper | Univ. Montreal / Univ. Sherbrooke [ ] | ||
Paper | MPI / Berkeley [ ] | ||
Paper | Univ. Toronto / MIT [ ] | ||
Paper | Univ. Montreal [ ] | ||
paper | TAU / USC [ ] | ||
[Web] | Virginia Tech / MSR | ||
[Web] | MPI / Berkeley | ||
[Paper] | Toronto | ||
[Paper] | Baidu / UCLA | ||
Paper | POSTECH [ ] [ ] | ||
Paper | CMU / Microsoft Research [ ] | ||
Paper | MetaMind [ ] | ||
Paper | SNU + NAVER [ ] | ||
Paper | UC Berkeley + Sony [ ] | ||
Paper | Postech [ ] | ||
Paper | SNU + NAVER [ ] | ||
Awesome Deep Vision / Papers / Image Generation / Convolutional / Recurrent Networks | |||
[Paper] | Aäron van den Oord, Nal Kalchbrenner, Oriol Vinyals, Lasse Espeholt, Alex Graves, Koray Kavukcuoglu. "Conditional Image Generation with PixelCNN Decoders" | ||
[Paper] | Alexey Dosovitskiy, Jost Tobias Springenberg, Thomas Brox, "Learning to Generate Chairs with Convolutional Neural Networks", CVPR, 2015 | ||
Paper | Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Jimenez Rezende, Daan Wierstra, "DRAW: A Recurrent Neural Network For Image Generation", ICML, 2015. [ ] | ||
Awesome Deep Vision / Papers / Image Generation / Adversarial Networks | |||
[Paper] | Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, Generative Adversarial Networks, NIPS, 2014 | ||
[Paper] | Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus, Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks, NIPS, 2015 | ||
Paper | Lucas Theis, Aäron van den Oord, Matthias Bethge, "A note on the evaluation of generative models", ICLR 2016. [ ] | ||
Paper | Zhenwen Dai, Andreas Damianou, Javier Gonzalez, Neil Lawrence, "Variationally Auto-Encoded Deep Gaussian Processes", ICLR 2016. [ ] | ||
Paper | Elman Mansimov, Emilio Parisotto, Jimmy Ba, Ruslan Salakhutdinov, "Generating Images from Captions with Attention", ICLR 2016, [ ] | ||
Paper | Jost Tobias Springenberg, "Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks", ICLR 2016, [ ] | ||
Paper | Harrison Edwards, Amos Storkey, "Censoring Representations with an Adversary", ICLR 2016, [ ] | ||
Paper | Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, Ken Nakae, Shin Ishii, "Distributional Smoothing with Virtual Adversarial Training", ICLR 2016, [ ] | ||
Paper | Jun-Yan Zhu, Philipp Krahenbuhl, Eli Shechtman, and Alexei A. Efros, "Generative Visual Manipulation on the Natural Image Manifold", ECCV 2016. [ ] [ ] [ ] | ||
Awesome Deep Vision / Papers / Image Generation / Mixing Convolutional and Adversarial Networks | |||
Paper | Alec Radford, Luke Metz, Soumith Chintala, "Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks", ICLR 2016. [ ] | ||
Awesome Deep Vision / Papers / Other Topics | |||
Paper | Visual Analogy [ ] | ||
[Paper] | Surface Normal Estimation | ||
[Paper] | Action Detection | ||
[Paper] | Crowd Counting | ||
[Paper] | 3D Shape Retrieval | ||
Awesome Deep Vision / Papers / Other Topics / Weakly-supervised Classification | |||
Paper | Samaneh Azadi, Jiashi Feng, Stefanie Jegelka, Trevor Darrell, "Auxiliary Image Regularization for Deep CNNs with Noisy Labels", ICLR 2016, [ ] | ||
Awesome Deep Vision / Papers / Other Topics | |||
[Paper] | Artistic Style | ||
Awesome Deep Vision / Papers / Other Topics / Human Gaze Estimation | |||
[Paper] | Xucong Zhang, Yusuke Sugano, Mario Fritz, Andreas Bulling, Appearance-Based Gaze Estimation in the Wild, CVPR, 2015 | ||
Awesome Deep Vision / Papers / Other Topics / Face Recognition | |||
[Paper] | Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf, DeepFace: Closing the Gap to Human-Level Performance in Face Verification, CVPR, 2014 | ||
[Paper] | Yi Sun, Ding Liang, Xiaogang Wang, Xiaoou Tang, DeepID3: Face Recognition with Very Deep Neural Networks, 2015 | ||
[Paper] | Florian Schroff, Dmitry Kalenichenko, James Philbin, FaceNet: A Unified Embedding for Face Recognition and Clustering, CVPR, 2015 | ||
Awesome Deep Vision / Papers / Other Topics / Facial Landmark Detection | |||
[Paper] | Yue Wu, Tal Hassner, KangGeon Kim, Gerard Medioni, Prem Natarajan, Facial Landmark Detection with Tweaked Convolutional Neural Networks, 2015 | ||
Awesome Deep Vision / Courses / Deep Vision | |||
CS231n: Convolutional Neural Networks for Visual Recognition | [Stanford] | ||
ELEG 5040: Advanced Topics in Signal Processing(Introduction to Deep Learning) | [CUHK] | ||
Awesome Deep Vision / Courses / More Deep Learning | |||
CS224d: Deep Learning for Natural Language Processing | [Stanford] | ||
Deep Learning by Prof. Nando de Freitas | [Oxford] | ||
Deep Learning by Prof. Yann LeCun | [NYU] | ||
Awesome Deep Vision / Books / Free Online Books | |||
Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville | |||
Neural Networks and Deep Learning by Michael Nielsen | |||
Deep Learning Tutorial by LISA lab, University of Montreal | |||
Awesome Deep Vision / Videos / Talks | |||
Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng | |||
Recent Developments in Deep Learning By Geoff Hinton | |||
The Unreasonable Effectiveness of Deep Learning by Yann LeCun | |||
Deep Learning of Representations by Yoshua bengio | |||
Awesome Deep Vision / Software / Framework | |||
Web | Tensorflow: An open source software library for numerical computation using data flow graph by Google [ ] | ||
Web | Torch7: Deep learning library in Lua, used by Facebook and Google Deepmind [ ] | ||
Awesome Deep Vision / Software / Framework / Web | |||
torchnet | 996 | over 5 years ago | Torch-based deep learning libraries: [ ], |
Awesome Deep Vision / Software / Framework | |||
Web | Caffe: Deep learning framework by the BVLC [ ] | ||
Web | Theano: Mathematical library in Python, maintained by LISA lab [ ] | ||
Awesome Deep Vision / Software / Framework / Web | |||
Pylearn2 | Theano-based deep learning libraries: [ ], [ ], [ ], [ ] | ||
Awesome Deep Vision / Software / Framework | |||
Web | MatConvNet: CNNs for MATLAB [ ] | ||
Web | MXNet: A flexible and efficient deep learning library for heterogeneous distributed systems with multi-language support [ ] | ||
Web | 1,784 | 5 months ago | Deepgaze: A computer vision library for human-computer interaction based on CNNs [ ] |
Awesome Deep Vision / Software / Applications / Adversarial Training | |||
[Web] | 3,861 | over 4 years ago | Code and hyperparameters for the paper "Generative Adversarial Networks" |
Awesome Deep Vision / Software / Applications / Understanding and Visualizing | |||
[Web] | 168 | about 7 years ago | Source code for "Understanding Deep Image Representations by Inverting Them," CVPR, 2015 |
Awesome Deep Vision / Software / Applications / Semantic Segmentation | |||
[Web] | 2,368 | over 7 years ago | Source code for the paper "Rich feature hierarchies for accurate object detection and semantic segmentation," CVPR, 2014 |
[Web] | 81 | about 7 years ago | Source code for the paper "Fully Convolutional Networks for Semantic Segmentation," CVPR, 2015 |
Awesome Deep Vision / Software / Applications / Super-Resolution | |||
[Web] | 27,495 | over 1 year ago | Image Super-Resolution for Anime-Style-Art |
Awesome Deep Vision / Software / Applications / Edge Detection | |||
[Web] | 94 | over 2 years ago | Source code for the paper "DeepContour: A Deep Convolutional Feature Learned by Positive-Sharing Loss for Contour Detection," CVPR, 2015 |
[Web] | 1,791 | 6 months ago | Source code for the paper "Holistically-Nested Edge Detection", ICCV 2015 |
Awesome Deep Vision / Tutorials | |||
Tutorial on Deep Learning in Computer Vision | [CVPR 2014] | ||
Applied Deep Learning for Computer Vision with Torch | 868 | almost 8 years ago | [CVPR 2015] |
Awesome Deep Vision / Blogs | |||
Deep down the rabbit hole: CVPR 2015 and beyond@Tombone's Computer Vision Blog | |||
CVPR recap and where we're going@Zoya Bylinskii (MIT PhD Student)'s Blog | |||
Facebook's AI Painting@Wired | |||
Inceptionism: Going Deeper into Neural Networks@Google Research | |||
Implementing Neural networks |
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