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We propose an action recognition framework using Gen- erative Adversarial Networks.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
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Alexander Klaser, Marcin Marszałek, and Cordelia Schmid · 2008
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Ivan Laptev, Marcin Marszalek, Cordelia Schmid, and Benjamin Rozenfeld · 2008
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Liblinear: A library for large linear classification
Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin · 2008
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Activity recognition using the velocity histories of tracked keypoints
Ross Messing, Chris Pal, and Henry Kautz · 2009
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Trajectons: Action recognition through the motion analysis of tracked features
Pyry Matikainen, Martial Hebert, and Rahul Sukthankar · 2009
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Hossein Mobahi, Ronan Collobert, and Jason Weston · 2009
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Alex Krizhevsky · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Convolutional learning of spatio-temporal features
Graham W Taylor, Rob Fergus, Yann LeCun, and Christoph Bregler · 2010
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Sequential deep learning for human action recognition
Moez Baccouche, Franck Mamalet, Christian Wolf, Christophe Garcia, and Atilla Baskurt · 2011
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H. Kuehne, H. Jhuang, E. Garrote, T. Poggio, and T. Serre · 2011
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Ucf101: A dataset of 101 human actions classes from videos in the wild
Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah · 2012
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Action bank: A high-level representation of activity in video
Sreemanananth Sadanand and Jason J Corso · 2012
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Trajectory-based modeling of human actions with motion reference points
Yu-Gang Jiang, Qi Dai, Xiangyang Xue, Wei Liu, and Chong-Wah Ngo · 2012
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Slow feature analysis for human action recognition
Zhang Zhang and Dacheng Tao · 2012
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Action recognition with improved trajectories
Heng Wang and Cordelia Schmid · 2013
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3d convolutional neural networks for human action recognition
Shuiwang Ji, Wei Xu, Ming Yang, and Kai Yu · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Two-stream convolutional networks for action recognition in videos
Karen Simonyan and Andrew Zisserman · 2014
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Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, and Yann LeCun · 2015
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Slow and steady feature analysis: higher order temporal coherence in video
Dinesh Jayaraman and Kristen Grauman · 2015
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Unsupervised learning of visual representations using videos
Xiaolong Wang and Abhinav Gupta · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
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Unsupervised and semi-supervised learning with categorical generative adversarial networks
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Large-scale video classification with convolutional neural networks
Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, and Li Fei-Fei · 2014
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Return of the devil in the details: Delving deep into convolutional nets
Ken Chatfield, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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Large-scale video classification with convolutional neural networks
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Beyond short snippets: Deep networks for video classification
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Empirical evaluation of rectified activations in convolutional network
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Shuffle and learn: unsupervised learning using temporal order verification
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Self-supervised video representation learning with odd-one-out networks
Basura Fernando, Hakan Bilen, Efstratios Gavves, and Stephen Gould · 2016
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Dynamic image networks for action recognition
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Generating videos with scene dynamics
Carl Vondrick, Hamed Pirsiavash, and Antonio Torralba · 2016
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2016
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Semi-supervised learning with generative adversarial networks
Augustus Odena · 2016
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Nips 2016 tutorial: Generative adversarial networks
Ian Goodfellow · 2016
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Deep networks with stochastic depth
Gao Huang, Yu Sun, Zhuang Liu, Daniel Sedra, and Kilian Q Weinberger · 2016
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Unsupervised representation learning by sorting sequences
Hsin-Ying Lee, Jia-Bin Huang, Maneesh Singh, and Ming-Hsuan Yang · 2017
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