Summary of Lfme: a Simple Framework For Learning From Multiple Experts in Domain Generalization, by Liang Chen et al.
LFME: A Simple Framework for Learning from Multiple Experts in Domain Generalization
by Liang Chen, Yong Zhang, Yibing Song, Zhiqiang Shen, Lingqiao Liu
First submitted to arxiv on: 22 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 This paper introduces a novel framework for domain generalization (DG) called learning from multiple experts (LFME). The goal of LFME is to train a target model that can perform well in unseen domains by leveraging knowledge from multiple source domains. Specifically, the framework involves training multiple experts, each specialized in a different domain, and using their output probabilities to regularize the logit of the target model during inference. This approach enables the target model to harness more information and mine hard samples from the experts during training. The authors demonstrate the effectiveness of LFME through extensive experiments on benchmarks from various DG tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LFME is a new way to help machines learn in new situations by learning from many different places they have learned before. It’s like having multiple teachers, each teaching you something different. Instead of just using one teacher’s advice, LFME combines the advice from all the teachers to help the machine make better decisions. This approach seems to work well and can even match the performance of other methods that are more complicated. |
Keywords
» Artificial intelligence » Domain generalization » Inference
