| Self-attention generative adversarial networks H Zhang, I Goodfellow, D Metaxas, A Odena International conference on machine learning, 7354-7363, 2019 | 4846 | 2019 |
| Conditional image synthesis with auxiliary classifier gans A Odena, C Olah, J Shlens International conference on machine learning, 2642-2651, 2017 | 4176 | 2017 |
| Deconvolution and checkerboard artifacts A Odena, V Dumoulin, C Olah Distill 1 (10), e3, 2016 | 1833 | 2016 |
| Realistic evaluation of deep semi-supervised learning algorithms A Oliver, A Odena, CA Raffel, ED Cubuk, I Goodfellow Advances in neural information processing systems 31, 2018 | 1249 | 2018 |
| Program synthesis with large language models J Austin, A Odena, M Nye, M Bosma, H Michalewski, D Dohan, E Jiang, ... arXiv preprint arXiv:2108.07732, 2021 | 1068 | 2021 |
| Semi-supervised learning with generative adversarial networks A Odena arXiv preprint arXiv:1606.01583, 2016 | 885 | 2016 |
| Show your work: Scratchpads for intermediate computation with language models M Nye, AJ Andreassen, G Gur-Ari, H Michalewski, J Austin, D Bieber, ... arXiv preprint arXiv:2112.00114, 2021 | 517 | 2021 |
| Tensorfuzz: Debugging neural networks with coverage-guided fuzzing A Odena, C Olsson, D Andersen, I Goodfellow International Conference on Machine Learning, 4901-4911, 2019 | 376 | 2019 |
| Consistency regularization for generative adversarial networks H Zhang, Z Zhang, A Odena, H Lee arXiv preprint arXiv:1910.12027, 2019 | 316 | 2019 |
| Discriminator rejection sampling S Azadi, C Olsson, T Darrell, I Goodfellow, A Odena arXiv preprint arXiv:1810.06758, 2018 | 158 | 2018 |
| Improved consistency regularization for gans Z Zhao, S Singh, H Lee, Z Zhang, A Odena, H Zhang Proceedings of the AAAI conference on artificial intelligence 35 (12), 11033 …, 2021 | 149 | 2021 |
| Is generator conditioning causally related to GAN performance? A Odena, J Buckman, C Olsson, T Brown, C Olah, C Raffel, I Goodfellow International conference on machine learning, 3849-3858, 2018 | 143 | 2018 |
| Deconvolution and checkerboard artifacts. Distill (2016) A Odena, V Dumoulin, C Olah | 127 | 2016 |
| Small-gan: Speeding up gan training using core-sets S Sinha, H Zhang, A Goyal, Y Bengio, H Larochelle, A Odena International Conference on Machine Learning, 9005-9015, 2020 | 81 | 2020 |
| Your local GAN: Designing two dimensional local attention mechanisms for generative models G Daras, A Odena, H Zhang, AG Dimakis Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 77 | 2020 |
| Top-k training of gans: Improving gan performance by throwing away bad samples S Sinha, Z Zhao, AG ALIAS PARTH GOYAL, CA Raffel, A Odena Advances in Neural Information Processing Systems 33, 14638-14649, 2020 | 63 | 2020 |
| Semi-supervised learning with generative adversarial networks. arXiv A Odena arXiv preprint arXiv:1606.01583 10, 2016 | 56 | 2016 |
| Changing model behavior at test-time using reinforcement learning A Odena, D Lawson, C Olah arXiv preprint arXiv:1702.07780, 2017 | 54 | 2017 |
| Open questions about generative adversarial networks A Odena Distill 4 (4), e18, 2019 | 53* | 2019 |
| Bustle: Bottom-up program synthesis through learning-guided exploration A Odena, K Shi, D Bieber, R Singh, C Sutton, H Dai arXiv preprint arXiv:2007.14381, 2020 | 46 | 2020 |
Ian GoodfellowDeepMindVerified email at deepmind.com
