Summary of Reward Incremental Learning in Text-to-image Generation, by Maorong Wang et al.
Reward Incremental Learning in Text-to-Image Generation
by Maorong Wang, Jiafeng Mao, Xueting Wang, Toshihiko Yamasaki
First submitted to arxiv on: 26 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 paper introduces Reward Incremental Learning (RIL), a challenging problem that requires text-to-image models to adapt to multiple downstream objectives incrementally. Existing methods are limited to single-reward tasks, restricting their applicability in real-world scenarios. The authors propose Reward Incremental Distillation (RID) to mitigate catastrophic forgetting, enabling stable performance across sequential reward tasks. RID is shown to achieve consistent, high-quality generation in RIL scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about teaching machines to generate images that meet specific criteria, like what people find aesthetically pleasing. Currently, large-scale models do a great job of generating general images, but they need fine-tuning to adapt to specific goals. Researchers have found ways to help these models learn, but there’s still a problem: when new objectives are introduced, the model forgets how to generate good images earlier on. The authors introduce a solution called Reward Incremental Distillation (RID) that helps prevent this forgetting and keeps generating high-quality images. |
Keywords
» Artificial intelligence » Distillation » Fine tuning
