Summary of Continual Learning on a Data Diet, by Elif Ceren Gok Yildirim et al.
Continual Learning on a Data Diet
by Elif Ceren Gok Yildirim, Murat Onur Yildirim, Joaquin Vanschoren
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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, the authors explore the concept of Continual Learning (CL), where models learn from important samples while ignoring redundant information. They draw inspiration from human cognition and propose an empirical study on coreset selection techniques for CL. The authors train different continual learners on increasing amounts of selected samples and investigate their learning-forgetting dynamics, presenting significant observations that learning from selectively chosen samples enhances incremental accuracy, improves knowledge retention, and refines learned representations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how models can learn more efficiently by focusing on important data points, rather than trying to use all available information. The authors tested different continual learning methods and found that they work better when using a smaller number of carefully chosen samples. This could be useful in situations where you have a lot of data, but not all of it is relevant or helpful. |
Keywords
» Artificial intelligence » Continual learning
