Summary of Model Developmental Safety: a Retention-centric Method and Applications in Vision-language Models, by Gang Li et al.
Model Developmental Safety: A Retention-Centric Method and Applications in Vision-Language Models
by Gang Li, Wendi Yu, Yao Yao, Wei Tong, Yingbin Liang, Qihang Lin, Tianbao Yang
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Optimization and Control (math.OC); Machine Learning (stat.ML)
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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 In this paper, researchers tackle the challenge of catastrophic forgetting in continual learning systems. Existing approaches focus on balancing performance on old and new tasks, but these methods are insufficient for safety-critical domains where preserving previous capabilities is crucial. To address this issue, the authors introduce model developmental safety, which ensures that new models strictly preserve existing protected capabilities while improving performance on target tasks. The team presents a retention-centric framework to formulate data-dependent constraints, which they use to develop a CLIP model for acquiring or improving image classification capabilities. They propose an efficient constrained optimization algorithm with theoretical guarantees and demonstrate its effectiveness in promoting model developmental safety through experiments on autonomous driving and scene recognition datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how artificial intelligence (AI) systems can keep learning new things without forgetting what they already know. When AI systems learn, they sometimes forget skills they had before, which is a problem because it makes them less accurate or reliable. The authors of this paper want to fix this issue by making sure that the AI system always keeps its old skills while also getting better at new tasks. They do this by creating a special framework for learning that ensures the AI system doesn’t forget what it knows. They tested their approach on some real-world problems, like recognizing images and driving cars, and it worked well. |
Keywords
» Artificial intelligence » Continual learning » Image classification » Optimization
