Amund Tveit's Blog

The 5th International Conference on Learning Representation (ICLR 2017) is coming to Toulon, France (April 24-26 2017).

This blog post gives an overview of Deep Learning with Generative and Adverserial Networks related papers submitted to ICLR 2017, see underneath for the list of papers. Want to learn about these topics? See OpenAI’s article about Generative Models and Ian Goodfellow et.al’s paper about Generative Adversarial Networks.

Best regards,

Amund Tveit

ICLR 2017 – Generative and Generative Adversarial Papers

1. Unsupervised Learning Using Generative Adversarial Training And Clustering – Authors: Vittal Premachandran, Alan L. Yuille
2. Improving Generative Adversarial Networks with Denoising Feature Matching – Authors: David Warde-Farley, Yoshua Bengio
3. Generative Adversarial Parallelization – Authors: Daniel Jiwoong Im, He Ma, Chris Dongjoo Kim, Graham Taylor
4. b-GAN: Unified Framework of Generative Adversarial Networks – Authors: Masatosi Uehara, Issei Sato, Masahiro Suzuki, Kotaro Nakayama, Yutaka Matsuo
5. Generative Adversarial Networks as Variational Training of Energy Based Models – Authors: Shuangfei Zhai, Yu Cheng, Rogerio Feris, Zhongfei Zhang
6. Boosted Generative Models – Authors: Aditya Grover, Stefano Ermon
7. Adversarial examples for generative models – Authors: Jernej Kos, Dawn Song
8. Mode Regularized Generative Adversarial Networks – Authors: Tong Che, Yanran Li, Athul Jacob, Yoshua Bengio, Wenjie Li
9. Variational Recurrent Adversarial Deep Domain Adaptation – Authors: Sanjay Purushotham, Wilka Carvalho, Tanachat Nilanon, Yan Liu
10. Structured Interpretation of Deep Generative Models – Authors: N. Siddharth, Brooks Paige, Alban Desmaison, Jan-Willem van de Meent, Frank Wood, Noah D. Goodman, Pushmeet Kohli, Philip H.S. Torr
11. Inference and Introspection in Deep Generative Models of Sparse Data – Authors: Rahul G. Krishnan, Matthew Hoffman
12. Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy – Authors: Dougal J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alex Smola, Arthur Gretton
13. Unsupervised sentence representation learning with adversarial auto-encoder – Authors: Shuai Tang, Hailin Jin, Chen Fang, Zhaowen Wang
14. Unsupervised Program Induction with Hierarchical Generative Convolutional Neural Networks – Authors: Qucheng Gong, Yuandong Tian, C. Lawrence Zitnick
15. A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Noise – Authors: Beilun Wang, Ji Gao, Yanjun Qi
16. On the Quantitative Analysis of Decoder-Based Generative Models – Authors: Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger Grosse
17. Evaluation of Defensive Methods for DNNs against Multiple Adversarial Evasion Models – Authors: Xinyun Chen, Bo Li, Yevgeniy Vorobeychik
18. Calibrating Energy-based Generative Adversarial Networks – Authors: Zihang Dai, Amjad Almahairi, Philip Bachman, Eduard Hovy, Aaron Courville
19. Inverse Problems in Computer Vision using Adversarial Imagination Priors – Authors: Hsiao-Yu Fish Tung, Katerina Fragkiadaki
20. Towards Principled Methods for Training Generative Adversarial Networks – Authors: Martin Arjovsky, Leon Bottou
21. Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning – Authors: Dilin Wang, Qiang Liu
22. Multi-view Generative Adversarial Networks – Authors: Mickaël Chen, Ludovic Denoyer
23. LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation – Authors: Jianwei Yang, Anitha Kannan, Dhruv Batra, Devi Parikh
24. Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks – Authors: Emily Denton, Sam Gross, Rob Fergus
25. Generative Adversarial Networks for Image Steganography – Authors: Denis Volkhonskiy, Boris Borisenko, Evgeny Burnaev
26. Unrolled Generative Adversarial Networks – Authors: Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein
27. Generative Multi-Adversarial Networks – Authors: Ishan Durugkar, Ian Gemp, Sridhar Mahadevan
28. Joint Multimodal Learning with Deep Generative Models – Authors: Masahiro Suzuki, Kotaro Nakayama, Yutaka Matsuo
29. Fast Adaptation in Generative Models with Generative Matching Networks – Authors: Sergey Bartunov, Dmitry P. Vetrov
30. Adversarially Learned Inference – Authors: Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Alex Lamb, Martin Arjovsky, Olivier Mastropietro, Aaron Courville
31. Perception Updating Networks: On architectural constraints for interpretable video generative models – Authors: Eder Santana, Jose C Principe
32. Energy-based Generative Adversarial Networks – Authors: Junbo Zhao, Michael Mathieu, Yann LeCun
33. Simple Black-Box Adversarial Perturbations for Deep Networks – Authors: Nina Narodytska, Shiva Kasiviswanathan
34. Learning in Implicit Generative Models – Authors: Shakir Mohamed, Balaji Lakshminarayanan
35. On Detecting Adversarial Perturbations – Authors: Jan Hendrik Metzen, Tim Genewein, Volker Fischer, Bastian Bischoff
36. Delving into Transferable Adversarial Examples and Black-box Attacks – Authors: Yanpei Liu, Xinyun Chen, Chang Liu, Dawn Song
37. Adversarial Feature Learning – Authors: Jeff Donahue, Philipp Krähenbühl, Trevor Darrell
38. Generative Paragraph Vector – Authors: Ruqing Zhang, Jiafeng Guo, Yanyan Lan, Jun Xu, Xueqi Cheng
39. Adversarial Machine Learning at Scale – Authors: Alexey Kurakin, Ian J. Goodfellow, Samy Bengio
40. Adversarial Training Methods for Semi-Supervised Text Classification – Authors: Takeru Miyato, Andrew M. Dai, Ian Goodfellow
41. Sampling Generative Networks: Notes on a Few Effective Techniques – Authors: Tom White
42. Adversarial examples in the physical world – Authors: Alexey Kurakin, Ian J. Goodfellow, Samy Bengio
43. Improving Sampling from Generative Autoencoders with Markov Chains – Authors: Kai Arulkumaran, Antonia Creswell, Anil Anthony Bharath
44. Neural Photo Editing with Introspective Adversarial Networks – Authors: Andrew Brock, Theodore Lim, J.M. Ritchie, Nick Weston
45. Learning to Protect Communications with Adversarial Neural Cryptography – Authors: Martín Abadi, David G. Andersen