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We introduce a method to disentangle controllable and uncontrollable factors of variation by interacting with the world.
Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Learning to disentangle factors of variation with manifold interaction
Scott Reed, Kihyuk Sohn, Yuting Zhang, and Honglak Lee · 2014
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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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Chainer: a next-generation open source framework for deep learning
Seiya Tokui, Kenta Oono, Shohei Hido, and Justin Clayton · 2015
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Deep recurrent q-learning for partially observable mdps
Matthew Hausknecht and Peter Stone · 2015
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Disentangling factors of variation in deep representation using adversarial training
Michael F Mathieu, Junbo Jake Zhao, Junbo Zhao, Aditya Ramesh, Pablo Sprechmann, and Yann LeCun · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2016
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Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Generative adversarial imitation learning
Jonathan Ho and Stefano Ermon · 2016
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Xi Chen, Diederik P Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, and Pieter Abbeel · 2016
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Generating images with perceptual similarity metrics based on deep networks
Alexey Dosovitskiy and Thomas Brox · 2016
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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Infogail: Interpretable imitation learning from visual demonstrations
Yunzhu Li, Jiaming Song, and Stefano Ermon · 2017
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Disentangled representation learning gan for pose-invariant face recognition
Luan Tran, Xi Yin, and Xiaoming Liu · 2017
Cited alongside, same era.
Disentangling the independently controllable factors of variation by interacting with the world
Valentin Thomas, Emmanuel Bengio, William Fedus, Jules Pondard, Philippe Beaudoin, Hugo Larochelle, Joelle Pineau, Doina Precup, and Yoshua Bengio · 2017
Cited alongside, same era.
Independently controllable factors
Valentin Thomas, Jules Pondard, Emmanuel Bengio, Marc Sarfati, Philippe Beaudoin, Marie-Jean Meurs, Joelle Pineau, Doina Precup, and Yoshua Bengio · 2017
A disentangled recognition and nonlinear dynamics model for unsupervised learning
Marco Fraccaro, Simon Kamronn, Ulrich Paquet, and Ole Winther · 2017
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Unsupervised learning of disentangled and interpretable representations from sequential data
Wei-Ning Hsu, Yu Zhang, and James Glass · 2017
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Disentangling dynamics and content for control and planning
Ershad Banijamali, Ahmad Khajenezhad, Ali Ghodsi, and Mohammad Ghavamzadeh · 2017
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Curiosity-driven exploration by self-supervised prediction
Deepak Pathak, Pulkit Agrawal, Alexei A Efros, and Trevor Darrell · 2017
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Independently controllable features
Emmanuel Bengio, Valentin Thomas, Joelle Pineau, Doina Precup, and Yoshua Bengio · 2017
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Network dissection: Quantifying interpretability of deep visual representations
David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Learning disentangled representations with semi-supervised deep generative models
Siddharth Narayanaswamy, T. Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah Goodman, Pushmeet Kohli, Frank Wood, and Philip Torr · 2017
Cited alongside, same era.
Reconstruction-based disentanglement for pose-invariant face recognition
Xi Peng, Xiang Yu, Kihyuk Sohn, Dimitris N Metaxas, and Manmohan Chandraker · 2017
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Guiding infogan with semi-supervision
Adrian Spurr, Emre Aksan, and Otmar Hilliges · 2017
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Unsupervised learning of disentangled representations from video
Emily L Denton et al · 2017
Cited alongside, same era.
Towards deeper understanding of variational autoencoding models
Shengjia Zhao, Jiaming Song, and Stefano Ermon · 2017
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A survey of methods for explaining black box models
Riccardo Guidotti, Anna Monreale, Franco Turini, Dino Pedreschi, and Fosca Giannotti · 2018
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Isolating sources of disentanglement in variational autoencoders
Tian Qi Chen, Xuechen Li, Roger Grosse, and David Duvenaud · 2018
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Joint-vae: Learning disentangled joint continuous and discrete representations
Emilien Dupont · 2018
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Visual Interpretability for Deep Learning: a Survey
Quanshi Zhang and Song-Chun Zhu · 2018
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Hyunjik Kim and Andriy Mnih · 2018
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Universal agent for disentangling environments and tasks
Jiayuan Mao, Honghua Dong, and Joseph J. Lim · 2018
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