| 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 | 348 | 2016 |
| Triple generative adversarial nets LI Chongxuan, T Xu, J Zhu, B Zhang Advances in neural information processing systems, 4088-4098, 2017 | 341* | 2017 |
| Max-margin deep generative models C Li, J Zhu, T Shi, B Zhang arXiv preprint arXiv:1504.06787, 2015 | 36 | 2015 |
| 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 | 34 | 2017 |
| Learning to generate with memory C Li, J Zhu, B Zhang International Conference on Machine Learning, 1177-1186, 2016 | 34 | 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 | 31 | 2017 |
| Graphical generative adversarial networks C Li, M Welling, J Zhu, B Zhang arXiv preprint arXiv:1804.03429, 2018 | 23 | 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 | 20 | 2018 |
| Triple generative adversarial networks C Li, K Xu, J Liu, J Zhu, B Zhang arXiv preprint arXiv:1912.09784, 2019 | 11 | 2019 |
| 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 | 10* | 2019 |
| Multi-objects generation with amortized structural regularization K Xu, C Li, J Zhu, B Zhang arXiv preprint arXiv:1906.03923, 2019 | 8 | 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 | 8 | 2018 |
| Understanding and Stabilizing GANs' Training Dynamics with Control Theory K Xu, C Li, J Zhu, B Zhang arXiv preprint arXiv:1909.13188, 2019 | 7 | 2019 |
| 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 | 5 | 2020 |
| MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering TW Tsai, C Li, J Zhu International Conference on Learning Representations, 2020 | 5 | 2020 |
| 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 | 5 | 2020 |
| Implicit Normalizing Flows C Lu, J Chen, C Li, Q Wang, J Zhu arXiv preprint arXiv:2103.09527, 2021 | 4 | 2021 |
| 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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 3 | 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 | 3 | 2020 |
| Countering noisy labels by learning from auxiliary clean labels TW Tsai, C Li, J Zhu arXiv preprint arXiv:1905.13305, 2019 | 3 | 2019 |
Jun ZhuProfessor of Computer Science, Tsinghua UniversityZweryfikowany adres z mail.tsinghua.edu.cn
