Self-supervised gans via auxiliary rotation loss

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2019-June, Issue , 2019, Pages 12146-12155

  Chen, Ting ^a   Zhai, Xiaohua ^b   Ritter, Marvin ^b   Lucic, Mario ^b   Houlsby, Neil ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b GOOGLE INC   (United States)

Author keywords

Deep Learning; Image and Video Synthesis; Representation Learning

Indexed keywords

COMPUTER VISION; UNSUPERVISED LEARNING;

FEATURE REPRESENTATION; IMAGE REPRESENTATIONS; NATURAL IMAGES; REPRESENTATION LEARNING; ROTATION LOSS; STATE OF THE ART; SYNTHESIZED IMAGES; VIDEO SYNTHESIS;

DEEP LEARNING;

EID: 85078017662     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2019.01243     Document Type: Conference Paper

References (46)

1. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems (NIPS), 2014.
2. Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. In Advances in Neural Information Processing Systems (NIPS), 2016.
3. Lars Mescheder, Andreas Geiger, and Sebastian Nowozin. Which training methods for gans do actually converge? In International Conference on Machine Learning (ICML), 2018.
4. Xudong Mao, Qing Li, Haoran Xie, Raymond YK Lau, Zhen Wang, and Stephen Paul Smolley. Least squares generative adversarial networks. In International Conference on Computer Vision (ICCV), 2016.
5. Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville. Improved training ofWasserstein GANs. Advances in Neural Information Processing Systems (NIPS), 2017.
6. Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. Spectral normalization for generative adversarial networks. International Conference on Learning Representations (ICLR), 2018.
7. Ting Chen, Mario Lucic, Neil Houlsby, and Sylvain Gelly. On Self Modulation for Generative Adversarial Networks. In International Conference on Learning Representations (ICLR), 2019.
8. Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. In International Conference on Learning Representations (ICLR), 2016.
9. Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena. Self-Attention generative adversarial networks. arXiv preprint arXiv: 1805.08318, 2018.
10. Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. In International Conference on Learning Representations (ICLR), 2018.
11. James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al. Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences, 2017.
12. Michael McCloskey and Neal J Cohen. Catastrophic interference in connectionist networks: The sequential learning problem. In Psychology of learning and motivation, volume 24. Elsevier, 1989.
13. Robert M French. Catastrophic forgetting in connectionist networks. Trends in cognitive sciences, 1999.
14. Hoang Thanh-Tung, Truyen Tran, and Svetha Venkatesh. On catastrophic forgetting and mode collapse in generative adversarial networks. ICML Workshop on Theoretical Foundations and Applications of Deep Generative Models, 2018.
15. Kevin J Liang, Chunyuan Li, Guoyin Wang, and Lawrence Carin. Generative Adversarial Network Training is a Continual Learning Problem. arXiv preprint arXiv: 1811.11083, 2018.
16. Ari Seff, Alex Beatson, Daniel Suo, and Han Liu. Continual learning in Generative Adversarial Nets. arXiv preprint arXiv: 1705.08395, 2017.
17. Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Joshua Susskind, Wenda Wang, and Russell Webb. Learning from simulated and unsupervised images through adversarial training. In Computer Vision and Pattern Recognition (CVPR), 2017.
18. Youngjin Kim, Minjung Kim, and Gunhee Kim. Memorization precedes generation: Learning unsupervised gans with memory networks. International Conference on Learning Representations (ICLR), 2018.
19. Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi, Thomas Hofmann, and Andreas Krause. An online learning approach to generative adversarial networks. In International Conference on Learning Representations (ICLR), 2018.
20. Augustus Odena, Christopher Olah, and Jonathon Shlens. Conditional image synthesis with auxiliary classifier GANs. In International Conference on Machine Learning (ICML), 2017.
21. Takeru Miyato and Masanori Koyama. cgans with projection discriminator. International Conference on Learning Representations (ICLR), 2018.
22. Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv: 1809.11096, 2018.
23. Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox. Discriminative unsupervised feature learning with convolutional neural networks. In Advances in Neural Information Processing Systems (NIPS), 2014.
24. Carl Doersch, Abhinav Gupta, and Alexei A Efros. Unsupervised visual representation learning by context prediction. In International Conference on Computer Vision (ICCV), 2015.
25. Richard Zhang, Phillip Isola, and Alexei A Efros. Colorful image colorization. In European Conference on Computer Vision (ECCV), 2016.
26. Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. In International Conference on Learning Representations (ICLR), 2018.
27. Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv: 1506.03365, 2015.
28. Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Günter Klambauer, and Sepp Hochreiter. GANs trained by a two time-scale update rule converge to a Nash equilibrium. In Advances in Neural Information Processing Systems (NIPS), 2017.
29. Mehdi SM Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly. Assessing generative models via precision and recall. In Advances in Neural Information Processing Systems (NIPS), 2018.
30. Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet. Are GANs Created Equal? A Largescale Study. In Advances in Neural Information Processing Systems (NIPS), 2018.
31. Shane Barratt and Rishi Sharma. A note on the inception score. arXiv preprint arXiv: 1801.01973, 2018.
32. Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, and Sylvain Gelly. The GAN Landscape: Losses, architectures, regularization, and normalization. arXiv preprint arXiv: 1807.04720, 2018.
33. Zhiming Zhou, Yuxuan Song, Lantao Yu, and Yong Yu. Understanding the effectiveness of lipschitz constraint in training of gans via gradient analysis. arXiv preprint arXiv: 1807.00751, 2018.
34. Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell. Adversarial feature learning. In International Conference on Learning Representations (ICLR), 2017.
35. Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. Deep clustering for unsupervised learning of visual features. European Conference on Computer Vision (ECCV), 2018.
36. Richard Zhang, Phillip Isola, and Alexei A Efros. Split-brain autoencoders: Unsupervised learning by cross-channel prediction. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR), 2017.
37. Friedemann Zenke, Ben Poole, and Surya Ganguli. Continual learning through synaptic intelligence. In International Conference on Machine Learning (ICML), 2017.
38. Harm De Vries, Florian Strub, Jérémie Mary, Hugo Larochelle, Olivier Pietquin, and Aaron C Courville. Modulating early visual processing by language. In Advances in Neural Information Processing Systems (NIPS), 2017.
39. Pulkit Agrawal, Joao Carreira, and Jitendra Malik. Learning to see by moving. In International Conference on Computer Vision (ICCV), 2015.
40. Hsin-Ying Lee, Jia-Bin Huang, Maneesh Singh, and Ming-Hsuan Yang. Unsupervised representation learning by sorting sequences. In International Conference on Computer Vision (ICCV), 2017.
41. Eric Jang, Coline Devin, Vincent Vanhoucke, and Sergey Levine. Grasp2vec: Learning object representations from self-supervised grasping. In Conference on Robot Learning (CoRL, 2018.
42. Lerrel Pinto and Abhinav Gupta. Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours. In International Conference on Robotics and Automation (ICRA), 2016.
43. T Nathan Mundhenk, Daniel Ho, and Barry Y Chen. Improvements to context based self-supervised learning. In Computer Vision and Pattern Recognition (CVPR), 2018.
44. Mehdi Noroozi and Paolo Favaro. Unsupervised learning of visual representations by solving jigsaw puzzles. In European Conference on Computer Vision (ECCV), 2016.
45. Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros. Context encoders: Feature learning by inpainting. In Computer Vision and Pattern Recognition (CVPR), 2016.
46. Alexander Kolesnikov, Xiaohua Zhai, and Lucas Beyer. Revisiting self-supervised visual representation learning. In Computer Vision and Pattern Recognition (CVPR), 2019.