Summary of Lifelong Learning and Selective Forgetting Via Contrastive Strategy, by Lianlei Shan et al.
Lifelong Learning and Selective Forgetting via Contrastive Strategy
by Lianlei Shan, Wenzhang Zhou, Wei Li, Xingyu Ding
First submitted to arxiv on: 28 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 framework for Learning with Selective Forgetting (LSF) utilizes a contrastive strategy to enable models to retain good performance on preserved tasks while selectively forgetting undesirable knowledge. The approach involves compacting features from same-class samples for preserved classes, and dispersing features from same-class samples for deleted classes to disrupt regular responses. This allows the model to forget specific classes without affecting its overall performance. The method achieves state-of-the-art results on four benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new framework is developed for Learning with Selective Forgetting (LSF), which helps machines learn and remember things in a way that lets them forget unwanted knowledge. The idea is to make features from same-class samples look similar when you want the model to remember, but make them look very different when you want it to forget. This way, the model can forget specific information without affecting its overall performance. The new method works really well on several test datasets. |
