| Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps C Lu, Y Zhou, F Bao, J Chen, C Li, J Zhu NeurIPS 2022, 2022 | 1062 | 2022 |
| 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 | 619 | 2016 |
| Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation Z Wang, C Lu, Y Wang, F Bao, C Li, H Su, J Zhu Advances in Neural Information Processing Systems 36, 2024 | 612 | 2024 |
| Triple generative adversarial nets C Li, T Xu, J Zhu, B Zhang Advances in neural information processing systems 30, 2017 | 565 | 2017 |
| Dpm-solver++: Fast solver for guided sampling of diffusion probabilistic models C Lu, Y Zhou, F Bao, J Chen, C Li, J Zhu arXiv preprint arXiv:2211.01095, 2022 | 394 | 2022 |
| Analytic-dpm: an analytic estimate of the optimal reverse variance in diffusion probabilistic models F Bao, C Li, J Zhu, B Zhang International Conference on Learning Representations, 2022 | 317 | 2022 |
| All are worth words: A vit backbone for diffusion models F Bao, S Nie, K Xue, Y Cao, C Li, H Su, J Zhu Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2023 | 293* | 2023 |
| Egsde: Unpaired image-to-image translation via energy-guided stochastic differential equations M Zhao, F Bao, C Li, J Zhu NeurIPS 2022, 2022 | 188 | 2022 |
| On evaluating adversarial robustness of large vision-language models Y Zhao, T Pang, C Du, X Yang, C Li, NMM Cheung, M Lin Advances in Neural Information Processing Systems 36, 2024 | 149 | 2024 |
| One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale F Bao, S Nie, K Xue, C Li, S Pu, Y Wang, G Yue, Y Cao, H Su, J Zhu ICML, 2023, 2023 | 126 | 2023 |
| Memory replay with data compression for continual learning L Wang, X Zhang, K Yang, L Yu, C Li, L Hong, S Zhang, Z Li, Y Zhong, ... ICLR 2022, 2022 | 117 | 2022 |
| 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 | 95 | 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 Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 88 | 2021 |
| Mice: Mixture of contrastive experts for unsupervised image clustering TW Tsai, C Li, J Zhu International conference on learning representations, 2020 | 85 | 2020 |
| Estimating the optimal covariance with imperfect mean in diffusion probabilistic models F Bao, C Li, J Sun, J Zhu, B Zhang arXiv preprint arXiv:2206.07309, 2022 | 74 | 2022 |
| Crm: Single image to 3d textured mesh with convolutional reconstruction model Z Wang, Y Wang, Y Chen, C Xiang, S Chen, D Yu, C Li, H Su, J Zhu European Conference on Computer Vision, 57-74, 2025 | 73 | 2025 |
| Maximum likelihood training for score-based diffusion odes by high order denoising score matching C Lu, K Zheng, F Bao, J Chen, C Li, J Zhu International Conference on Machine Learning, 14429-14460, 2022 | 72 | 2022 |
| Efficient learning of generative models via finite-difference score matching T Pang, K Xu, C Li, Y Song, S Ermon, J Zhu Advances in Neural Information Processing Systems 33, 19175-19188, 2020 | 54 | 2020 |
| Equivariant Energy-Guided SDE for Inverse Molecular Design F Bao, M Zhao, Z Hao, P Li, C Li, J Zhu ICLR 2023, 2022 | 53 | 2022 |
| 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 | 53 | 2017 |
Jun ZhuProfessor of Computer Science, Tsinghua UniversityVerified email at mail.tsinghua.edu.cn
