Summary of Weighted Risk Invariance: Domain Generalization Under Invariant Feature Shift, by Gina Wong et al.
Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift
by Gina Wong, Joshua Gleason, Rama Chellappa, Yoav Wald, Anqi Liu
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper proposes a new approach to learning models that can generalize well to new environments without requiring additional training data. The key idea is to train models that are invariant to certain features of the input data, which helps them to extract more robust and transferable representations. However, previous methods for learning invariant models have been shown to underperform in certain situations, particularly when there are shifts in the marginal distribution of the extracted features. To address this issue, the authors propose a new framework called weighted risk invariance (WRI), which is designed to learn invariant models that can handle such shifts. The paper shows that WRI can provably learn invariant models in linear-Gaussian settings and outperforms previous methods in practice. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers are trying to create machine learning models that work well even when the data they’re given changes. They want these models to be “invariant” to certain things, like different lighting or backgrounds. But so far, their ideas haven’t been working as well as they hoped. In this paper, they propose a new way of doing things called “weighted risk invariance”. It’s designed to help the model learn what’s important and ignore what’s not. They show that this approach works better than previous methods in certain situations. |
Keywords
* Artificial intelligence * Machine learning
