Summary of On Transfer in Classification: How Well Do Subsets Of Classes Generalize?, by Raphael Baena et al.
On Transfer in Classification: How Well do Subsets of Classes Generalize?
by Raphael Baena, Lucas Drumetz, Vincent Gripon
First submitted to arxiv on: 6 Mar 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 This paper explores the theoretical foundations of transfer learning in classification, which enables models trained on a specific set of classes to generalize to new ones. Specifically, it establishes a partially ordered set of subsets of classes, allowing for the representation of which subset can generalize to others. The framework is applied in a practical setting to predict which subset of classes leads to the best performance when testing on all of them. Additionally, the paper examines few-shot learning, where transfer is crucial. Overall, this work contributes to a better understanding of transfer mechanics and model generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at why some models can learn from one set of classes and apply it to others without extra training. The authors want to understand how and why this works, so they create a tool that shows which groups of classes can be used together. They use this tool to predict when combining certain classes will lead to the best results. This research helps us better understand how models work and learn from new data. |
Keywords
* Artificial intelligence * Classification * Few shot * Generalization * Transfer learning
