Summary of Unlearning Concepts in Diffusion Model Via Concept Domain Correction and Concept Preserving Gradient, by Yongliang Wu et al.
Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient
by Yongliang Wu, Shiji Zhou, Mingzhuo Yang, Lianzhe Wang, Heng Chang, Wenbo Zhu, Xinting Hu, Xiao Zhou, Xu Yang
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to eliminate sensitive information from text-to-image diffusion models called DoCo (Domain Correction). The existing Machine Unlearning methods face two challenges: limited generalization and utility degradation. To address these issues, the proposed framework aligns the output domains of sensitive and anchor concepts through adversarial training, ensuring comprehensive unlearning of target concepts while preserving the model’s overall performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DoCo is a way to remove sensitive information from images generated by text-to-image models. These models are very good at creating realistic pictures, but they can also include private or harmful content if not designed carefully. To fix this problem, researchers have developed a method called Machine Unlearning (MU), which tries to eliminate the unwanted concepts from the model. However, current MU methods don’t work well because they either only remove sensitive information from specific types of prompts or make the model worse at doing its job. |
Keywords
» Artificial intelligence » Diffusion » Generalization
