Summary of Cat: Contrastive Adapter Training For Personalized Image Generation, by Jae Wan Park et al.
CAT: Contrastive Adapter Training for Personalized Image Generation
by Jae Wan Park, Sang Hyun Park, Jun Young Koh, Junha Lee, Min Song
First submitted to arxiv on: 11 Apr 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 A novel approach to personalizing image generation using diffusion models is presented, leveraging adapters from natural language processing. While this method allows for low-cost adaptation, it often leads to unsatisfactory outcomes, corrupting the backbone model’s prior knowledge and resulting in limited diversity in object generation. To address this issue, Contrastive Adapter Training (CAT) is proposed, which enhances adapter training through the application of CAT loss. This approach preserves the base model’s original knowledge when initiating adapters and is evaluated using the Knowledge Preservation Score (KPS). The effectiveness of CAT is demonstrated qualitatively and quantitatively, with potential applications in multi-concept adapter optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Image generation using diffusion models can be personalized at a low cost by applying adapters from natural language processing. However, this method often leads to unsatisfactory outcomes, resulting in limited diversity in object generation. To solve this problem, a new approach called Contrastive Adapter Training (CAT) is presented. This approach helps preserve the base model’s original knowledge and allows for better image generation. |
Keywords
» Artificial intelligence » Image generation » Natural language processing » Optimization
