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Please list adversarial papers to look at here.

Owner Paper Authors
James Image-to-Image Translation with Conditional Adversarial Networks Isola et al
Amos Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks (aka Eyescream) Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus
James (& Elliot) Generative Adversarial Nets Goodfellow et al
James A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models Finn et al
Gavin (? / Matt) Energy-based Generative Adversarial Networks Zhao, Mathieu and LeCun
Matt Generative Adversarial Networks as Variational Training of Energy Based Models Zhai, Cheng, Feris and Zhang
Matt Calibrating Energy-based Generative Adversarial Networks Dai, Almahairi, Bachman, Hovy and Courville
Matt Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning Wang and Liu
Antreas Unrolled Generative Adversarial Networks Luke Metz, Ben Poole, David Pfau, and Jascha Sohl-Dickstein
Antreas Connecting Generative Adversarial Networks and Actor-Critic Methods David Pfau, Oriol Vinyals
Antreas Generative Multi-Adversarial Networks Ishan Durugkar, Ian Gemp, Sridhar Mahadevan
Gavin Improved Techniques for Training GANs Salimans et al
Gavin Disentangling factors of variation in deep representations using adversarial training Mathieu et al
Gavin Conditional Image Synthesis with Auxiliary Classifier GANs Odena, Olah and Shlens
Gavin Synthesizing the preferred inputs for neurons in neural networks via deep generator networks Nguyen et al
Elliot Unsupervised Cross-Domain Image Generation Taigman, Polyak and Wolf
Elliot Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Alec Radford, Luke Metz, Soumith Chintala
Amos Infogan: Interpretable representation learning by information maximizing generative adversarial nets Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
Amos Domain-Adversarial Neural Networks Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand
Amos How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary? Ferenc Huszár
Amos Learning What and Where to Draw Scott Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee
Amos Why are deep nets reversible: A simple theory, with implications for training Sanjeev Arora, Yingyu Liang, Tengyu Ma
Amos Learning to Pivot with Adversarial Networks Gilles Louppe, Michael Kagan, Kyle Cranmer
-- AmosStorkey - 22 Nov 2016
Topic revision: r11 - 25 Nov 2016 - 13:20:27 - Main.s0816700
 
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