Summary of Towards Lifelong Few-shot Customization Of Text-to-image Diffusion, by Nan Song et al.
Towards Lifelong Few-Shot Customization of Text-to-Image Diffusion
by Nan Song, Xiaofeng Yang, Ze Yang, Guosheng Lin
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Lifelong Few-Shot Customization for text-to-image diffusion aims to continually generalize existing models for new tasks with minimal data while preserving old knowledge. The current customization diffusion models excel in few-shot tasks but struggle with catastrophic forgetting problems in lifelong generations. To address these challenges, the authors devise a data-free knowledge distillation strategy to retain previous concepts and an In-Context Generation paradigm that facilitates few-shot generation and mitigates previous concepts forgetting. Extensive experiments demonstrate the proposed Lifelong Few-Shot Diffusion method can produce high-quality and accurate images while maintaining previously learned knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to make AI models better at learning new things without forgetting what they already know. The current methods are great for small tasks but struggle when faced with many tasks over time. To solve this problem, the authors develop two new strategies: one that helps the model remember important details from previous tasks and another that lets the model generate new images based on context. This results in a more effective lifelong learning method that produces high-quality images. |
Keywords
» Artificial intelligence » Diffusion » Few shot » Knowledge distillation
