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Proceedings of the AAAI Conference on Artificial Intelligence

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TIDE: Temporal-Aware Sparse Autoencoders for Interpretabl...
Victor Shea- · 2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Victor Shea-Jay Huang Multimedia Laboratory (MMLab), The Chinese University of Hong Kong Shanghai Artificial Intelligence Laboratory
  • Le Zhuo Multimedia Laboratory (MMLab), The Chinese University of Hong Kong Shanghai Artificial Intelligence Laboratory
  • Yi Xin Shanghai Artificial Intelligence Laboratory
  • Zhaokai Wang Shanghai Artificial Intelligence Laboratory
  • Fu-Yun Wang Multimedia Laboratory (MMLab), The Chinese University of Hong Kong
  • Yuchi Wang Multimedia Laboratory (MMLab), The Chinese University of Hong Kong
  • Renrui Zhang Multimedia Laboratory (MMLab), The Chinese University of Hong Kong
  • Peng Gao Shanghai Artificial Intelligence Laboratory
  • Hongsheng Li Multimedia Laboratory (MMLab), The Chinese University of Hong Kong Shanghai Artificial Intelligence Laboratory CPII under InnoHK

DOI:

https://doi.org/10.1609/aaai.v40i1.37006

Abstract

Diffusion Transformers (DiTs) are a powerful yet underexplored class of generative models compared to U-Net-based diffusion architectures. We propose TIDE—Temporal-aware sparse autoencoders for Interpretable Diffusion transformErs—a framework designed to extract sparse, interpretable activation features across timesteps in DiTs. TIDE effectively captures temporally-varying representations and reveals that DiTs naturally learn hierarchical semantics (e.g., 3D structure, object class, and fine-grained concepts) during large-scale pretraining. Experiments show that TIDE enhances interpretability and controllability while maintaining reasonable generation quality, enabling applications such as safe image editing and style transfer.

How to Cite

Huang, V. S.-J., Zhuo, L., Xin, Y., Wang, Z., Wang, F.-Y., Wang, Y., … Li, H. (2026). TIDE: Temporal-Aware Sparse Autoencoders for Interpretable Diffusion Transformers in Image Generation. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 435–443. https://doi.org/10.1609/aaai.v40i1.37006

Issue

Section

AAAI Technical Track on Application Domains I

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