Summary of Invariant Correlation Of Representation with Label: Enhancing Domain Generalization in Noisy Environments, by Gaojie Jin et al.
Invariant Correlation of Representation with Label: Enhancing Domain Generalization in Noisy Environments
by Gaojie Jin, Ronghui Mu, Xinping Yi, Xiaowei Huang, Lijun Zhang
First submitted to arxiv on: 1 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 The Invariant Risk Minimization (IRM) approach aims to address the challenge of domain generalization by training a feature representation that remains invariant across multiple environments. The IRM-related techniques, such as IRMv1 and VREx, may struggle in noisy environments due to erroneous optimization directions. To overcome this issue, we introduce ICorr, a novel approach designed to surmount the above challenge in noisy settings. We also analyze why previous methods may lose ground while ICorr can succeed through a case study. Theoretically, from a causality perspective, we illustrate that the invariant correlation of representation with label is a necessary condition for the optimal invariant predictor in noisy environments. Empirically, we demonstrate the effectiveness of ICorr by comparing it with other domain generalization methods on various noisy datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Invariant Risk Minimization (IRM) approach tries to make sure that a model works well across different situations or environments. But sometimes, this approach can get stuck in noisy situations where there’s a lot of noise and uncertainty. To solve this problem, researchers came up with a new idea called ICorr, which is better at handling noisy situations. They also looked at why previous methods didn’t work as well and found that they needed to make sure the model was correlated with the correct answer in a way that stayed the same across different environments. |
Keywords
» Artificial intelligence » Domain generalization » Optimization
