Summary of Precise, Fast, and Low-cost Concept Erasure in Value Space: Orthogonal Complement Matters, by Yuan Wang et al.
Precise, Fast, and Low-cost Concept Erasure in Value Space: Orthogonal Complement Matters
by Yuan Wang, Ouxiang Li, Tingting Mu, Yanbin Hao, Kuien Liu, Xiang Wang, Xiangnan He
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed Adaptive Value Decomposer (AdaVD) method is a training-free, low-cost concept erasure technique designed to remove unwanted concepts from pre-trained diffusion models. This method improves upon existing solutions by achieving a precise removal of target concepts while minimizing impact on non-target content generation. AdaVD utilizes classical linear algebraic orthogonal complement operations in the value space of each cross-attention layer within the UNet architecture, adapting an erasure strength factor to navigate between erasure efficacy and prior preservation. Experimental results demonstrate AdaVD’s effectiveness for single and multiple concept erasure, showing a 2-10 fold improvement in prior preservation compared to state-of-the-art methods while maintaining high erasure efficacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to remove unwanted concepts from text-to-image generation models. This is important because the models can generate images that include things like copyrighted material or offensive language. The proposed method, called Adaptive Value Decomposer (AdaVD), doesn’t require training and is fast and low-cost. AdaVD works by using a classical math technique in each layer of the model to remove unwanted concepts while preserving other content. The results show that AdaVD is better than existing methods at removing unwanted concepts without losing important details. |
Keywords
» Artificial intelligence » Cross attention » Diffusion » Image generation » Unet
