Summary of Local Vs Global Continual Learning, by Giulia Lanzillotta et al.
Local vs Global continual learning
by Giulia Lanzillotta, Sidak Pal Singh, Benjamin F. Grewe, Thomas Hofmann
First submitted to arxiv on: 23 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 explore the problem of continual learning, which involves integrating new information into a model while retaining previously acquired knowledge. Despite progress made recently, the issue remains an open one. By analyzing the mechanisms behind successes and failures of existing algorithms, the authors aim to develop new strategies for effective continual learning. The study focuses on multi-task loss approximation, comparing local and global approximations as alternative approaches. It also classifies existing algorithms based on their approximation methods and investigates the practical implications of this distinction in common continual learning scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Continual learning is a way to teach machines to learn new things while keeping what they already know. Right now, it’s still a bit tricky to get machines to do this effectively. To improve our understanding of how to make this work better, researchers are studying the ways that different algorithms try to achieve continual learning. They’re looking at two main approaches: using information from a small area (local) or from a wider area (global). The team is also grouping existing algorithms based on which approach they use and seeing what happens when they apply these methods in real-life scenarios. |
Keywords
* Artificial intelligence * Continual learning * Multi task
