$\textit{Revelio}$: Interpreting and leveraging semantic information in diffusion models
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866908476163227648 |
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| author | Kim, Dahye Thomas, Xavier Ghadiyaram, Deepti |
| author_facet | Kim, Dahye Thomas, Xavier Ghadiyaram, Deepti |
| contents | We study $\textit{how}$ rich visual semantic information is represented within various layers and denoising timesteps of different diffusion architectures. We uncover monosemantic interpretable features by leveraging k-sparse autoencoders (k-SAE). We substantiate our mechanistic interpretations via transfer learning using light-weight classifiers on off-the-shelf diffusion models' features. On $4$ datasets, we demonstrate the effectiveness of diffusion features for representation learning. We provide an in-depth analysis of how different diffusion architectures, pre-training datasets, and language model conditioning impacts visual representation granularity, inductive biases, and transfer learning capabilities. Our work is a critical step towards deepening interpretability of black-box diffusion models. Code and visualizations available at: https://github.com/revelio-diffusion/revelio |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_16725 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | $\textit{Revelio}$: Interpreting and leveraging semantic information in diffusion models Kim, Dahye Thomas, Xavier Ghadiyaram, Deepti Computer Vision and Pattern Recognition We study $\textit{how}$ rich visual semantic information is represented within various layers and denoising timesteps of different diffusion architectures. We uncover monosemantic interpretable features by leveraging k-sparse autoencoders (k-SAE). We substantiate our mechanistic interpretations via transfer learning using light-weight classifiers on off-the-shelf diffusion models' features. On $4$ datasets, we demonstrate the effectiveness of diffusion features for representation learning. We provide an in-depth analysis of how different diffusion architectures, pre-training datasets, and language model conditioning impacts visual representation granularity, inductive biases, and transfer learning capabilities. Our work is a critical step towards deepening interpretability of black-box diffusion models. Code and visualizations available at: https://github.com/revelio-diffusion/revelio |
| title | $\textit{Revelio}$: Interpreting and leveraging semantic information in diffusion models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.16725 |