| Towards better analysis of deep convolutional neural networks M Liu, J Shi, Z Li, C Li, J Zhu, S Liu IEEE transactions on visualization and computer graphics 23 (1), 91-100, 2016 | 295 | 2016 |
| Triple generative adversarial nets LI Chongxuan, T Xu, J Zhu, B Zhang Advances in neural information processing systems, 4088-4098, 2017 | 268* | 2017 |
| Max-margin deep generative models C Li, J Zhu, T Shi, B Zhang arXiv preprint arXiv:1504.06787, 2015 | 32 | 2015 |
| Learning to generate with memory C Li, J Zhu, B Zhang International Conference on Machine Learning, 1177-1186, 2016 | 29 | 2016 |
| Max-margin deep generative models for (semi-) supervised learning C Li, J Zhu, B Zhang IEEE transactions on pattern analysis and machine intelligence 40 (11), 2762 …, 2017 | 27 | 2017 |
| Learning to write stylized chinese characters by reading a handful of examples D Sun, T Ren, C Li, H Su, J Zhu arXiv preprint arXiv:1712.06424, 2017 | 25 | 2017 |
| Graphical generative adversarial networks C Li, M Welling, J Zhu, B Zhang arXiv preprint arXiv:1804.03429, 2018 | 19 | 2018 |
| Collaborative filtering with user-item co-autoregressive models C Du, C Li, Y Zheng, J Zhu, B Zhang Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 12 | 2018 |
| To relieve your headache of training an mrf, take advil C Li, C Du, K Xu, M Welling, J Zhu, B Zhang arXiv preprint arXiv:1901.08400, 2019 | 9* | 2019 |
| Learning implicit generative models by teaching explicit ones C Du, K Xu, C Li, J Zhu, B Zhang arXiv preprint arXiv:1807.03870, 2018 | 6 | 2018 |
| Bi-level score matching for learning energy-based latent variable models F Bao, C Li, K Xu, H Su, J Zhu, B Zhang arXiv preprint arXiv:2010.07856, 2020 | 3 | 2020 |
| Multi-objects generation with amortized structural regularization K Xu, C Li, J Zhu, B Zhang arXiv preprint arXiv:1906.03923, 2019 | 3 | 2019 |
| Countering noisy labels by learning from auxiliary clean labels TW Tsai, C Li, J Zhu arXiv preprint arXiv:1905.13305, 2019 | 3 | 2019 |
| Efficient learning of generative models via finite-difference score matching T Pang, K Xu, C Li, Y Song, S Ermon, J Zhu arXiv preprint arXiv:2007.03317, 2020 | 2 | 2020 |
| Triple generative adversarial networks C Li, K Xu, J Liu, J Zhu, B Zhang arXiv preprint arXiv:1912.09784, 2019 | 2 | 2019 |
| Understanding and Stabilizing GANs' Training Dynamics with Control Theory K Xu, C Li, J Zhu, B Zhang arXiv preprint arXiv:1909.13188, 2019 | 2 | 2019 |
| Population matching discrepancy and applications in deep learning J Chen, C Li, Y Ru, J Zhu Proceedings of the 31st International Conference on Neural Information …, 2017 | 2 | 2017 |
| ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning L Wang, K Yang, C Li, L Hong, Z Li, J Zhu arXiv preprint arXiv:2101.00407, 2021 | | 2021 |
| Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptation Q Luo, Z Liu, L Hong, C Li, K Yang, L Wang, F Zhou, G Li, Z Li, J Zhu arXiv preprint arXiv:2101.01400, 2021 | | 2021 |
| Understanding and Stabilizing GANs’ Training Dynamics using Control Theory K Xu, C Li, J Zhu, B Zhang International Conference on Machine Learning, 10566-10575, 2020 | | 2020 |
Jun ZhuProfessor of Computer Science, Tsinghua UniversityVerified email at mail.tsinghua.edu.cn
