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Projects
- 1 Active
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CAREER: Learning Hierarchical Generative Models for Expressive, Controllable, and Cross-domain Representations
Han, T. (PI)
1/07/24 → 30/06/29
Project: Research project
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Learning Multimodal Latent Generative Models with Energy-Based Prior
Yuan, S., Cui, J., Li, H. & Han, T., 2025, Computer Vision – ECCV 2024 - 18th European Conference, Proceedings. Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T. & Varol, G. (eds.). p. 86-100 15 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 15147 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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RGPNet: Learning a detail-preserved progressive model for single image deraining based on gradient prior
Luo, Y., Liang, G., Ling, J., Zhou, T., Huang, H. & Han, T., Oct 2025, In: Digital Signal Processing: A Review Journal. 165, 105312.Research output: Contribution to journal › Article › peer-review
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Enforcing Sparsity on Latent Space for Robust and Explainable Representations
Li, H. & Han, T., 3 Jan 2024, Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024. p. 5270-5279 10 p. (Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
3 Scopus citations -
Layerwise Change of Knowledge in Neural Networks
Cheng, X., Cheng, L., Peng, Z., Xu, Y., Han, T. & Zhang, Q., 2024, In: Proceedings of Machine Learning Research. 235, p. 8038-8059 22 p.Research output: Contribution to journal › Conference article › peer-review
1 Scopus citations -
Learning Latent Space Hierarchical EBM Diffusion Models
Cui, J. & Han, T., 2024, In: Proceedings of Machine Learning Research. 235, p. 9633-9645 13 p.Research output: Contribution to journal › Conference article › peer-review