Concept-TRAK: Understanding how diffusion models learn concepts through concept-level attribution
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918364826304512 |
|---|---|
| author | Park, Yonghyun Lai, Chieh-Hsin Hayakawa, Satoshi Takida, Yuhta Murata, Naoki Liao, Wei-Hsiang Choi, Woosung Cheuk, Kin Wai Koo, Junghyun Mitsufuji, Yuki |
| author_facet | Park, Yonghyun Lai, Chieh-Hsin Hayakawa, Satoshi Takida, Yuhta Murata, Naoki Liao, Wei-Hsiang Choi, Woosung Cheuk, Kin Wai Koo, Junghyun Mitsufuji, Yuki |
| contents | While diffusion models excel at image generation, their growing adoption raises critical concerns about copyright issues and model transparency. Existing attribution methods identify training examples influencing an entire image, but fall short in isolating contributions to specific elements, such as styles or objects, that are of primary concern to stakeholders. To address this gap, we introduce concept-level attribution through a novel method called Concept-TRAK, which extends influence functions with a key innovation: specialized training and utility loss functions designed to isolate concept-specific influences rather than overall reconstruction quality. We evaluate Concept-TRAK on novel concept attribution benchmarks using Synthetic and CelebA-HQ datasets, as well as the established AbC benchmark, showing substantial improvements over prior methods in concept-level attribution scenarios. We further demonstrate its versatility on real-world text-to-image generation with compositional and multi-concept prompts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_06547 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Concept-TRAK: Understanding how diffusion models learn concepts through concept-level attribution Park, Yonghyun Lai, Chieh-Hsin Hayakawa, Satoshi Takida, Yuhta Murata, Naoki Liao, Wei-Hsiang Choi, Woosung Cheuk, Kin Wai Koo, Junghyun Mitsufuji, Yuki Computer Vision and Pattern Recognition Machine Learning While diffusion models excel at image generation, their growing adoption raises critical concerns about copyright issues and model transparency. Existing attribution methods identify training examples influencing an entire image, but fall short in isolating contributions to specific elements, such as styles or objects, that are of primary concern to stakeholders. To address this gap, we introduce concept-level attribution through a novel method called Concept-TRAK, which extends influence functions with a key innovation: specialized training and utility loss functions designed to isolate concept-specific influences rather than overall reconstruction quality. We evaluate Concept-TRAK on novel concept attribution benchmarks using Synthetic and CelebA-HQ datasets, as well as the established AbC benchmark, showing substantial improvements over prior methods in concept-level attribution scenarios. We further demonstrate its versatility on real-world text-to-image generation with compositional and multi-concept prompts. |
| title | Concept-TRAK: Understanding how diffusion models learn concepts through concept-level attribution |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2507.06547 |