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1606.03657
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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
12 June 2016
Xi Chen
Yan Duan
Rein Houthooft
John Schulman
Ilya Sutskever
Pieter Abbeel
GAN
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Papers citing
"InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets"
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Zihang Dai
Yulun Du
Eduard H. Hovy
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Ian S. Fischer
Jonathan Huang
Kevin Patrick Murphy
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Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets
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Yevgen Chebotar
S. Schaal
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Learning to Generate Chairs with Generative Adversarial Nets
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Zachary Chase Lipton
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Ayan Chakrabarti
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Wei-Chen Chiu
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Xiao-Jun Wu
Zhi-Qi Cheng
Hao Liu
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Vishnu Boddeti
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Stochastic Neural Networks for Hierarchical Reinforcement Learning
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Yan Duan
Pieter Abbeel
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Viveka Kulharia
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Yanwei Fu
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Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
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Zhao Hai
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Szu-Yu Chou
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InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations
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Jiaming Song
Stefano Ermon
160
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Shuai Zheng
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Expecting the Unexpected: Training Detectors for Unusual Pedestrians with Adversarial Imposters
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Deva Ramanan
172
54
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Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
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Moonsu Cha
Hyunsoo Kim
Jung Kwon Lee
Jiwon Kim
GAN
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419
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0
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End-to-end semantic face segmentation with conditional random fields as convolutional, recurrent and adversarial networks
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Yağmur Güçlütürk
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Sergio Escalera
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Jordi Gonzalez
R. Lier
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Sébastien Ehrhardt
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Triple Generative Adversarial Nets
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Zichao Yang
Xiaodan Liang
Ruslan Salakhutdinov
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Hierarchical Implicit Models and Likelihood-Free Variational Inference
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Age Progression/Regression by Conditional Adversarial Autoencoder
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Hairong Qi
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McGan: Mean and Covariance Feature Matching GAN
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169
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Learning Hierarchical Features from Generative Models
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Stefano Ermon
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Generative Adversarial Active Learning
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Generative Mixture of Networks
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Pascal Poupart
139
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Unlabeled Samples Generated by GAN Improve the Person Re-identification Baseline in vitro
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2,003
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OffRL
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Alan Ritter
Dan Jurafsky
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Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
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