Summary of Enabling Asymmetric Knowledge Transfer in Multi-task Learning with Self-auxiliaries, by Olivier Graffeuille et al.
Enabling Asymmetric Knowledge Transfer in Multi-Task Learning with Self-Auxiliaries
by Olivier Graffeuille, Yun Sing Koh, Joerg Wicker, Moritz Lehmann
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 paper investigates an understudied problem in multi-task learning: asymmetric task relationships, where some tasks benefit from positive transfer while others are hindered by negative transfer. The authors propose an optimization strategy that incorporates cloned tasks called self-auxiliaries to flexibly transfer knowledge between tasks. This approach can exploit asymmetric task relationships, leveraging positive transfer while avoiding negative transfer. Experimental results show substantial performance improvements on benchmark computer vision problems compared to existing multi-task optimization strategies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how different learning tasks are related in multi-task learning. Usually, when we learn multiple things together, it’s either all good or all bad. But what if some tasks help each other and others hurt each other? The authors came up with a new way to learn that takes these tricky relationships into account. They added special “helping” tasks called self-auxiliaries to the learning process, which lets them share knowledge between tasks in a smart way. This helps improve results on computer vision problems. |
Keywords
» Artificial intelligence » Multi task » Optimization
