Summary of A Survey on Evaluation Of Out-of-distribution Generalization, by Han Yu et al.
A Survey on Evaluation of Out-of-Distribution Generalization
by Han Yu, Jiashuo Liu, Xingxuan Zhang, Jiayun Wu, Peng Cui
First submitted to arxiv on: 4 Mar 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 Machine learning educators can expect that this paper tackles the critical issue of out-of-distribution (OOD) generalization in machine learning models. The authors argue that current models are prone to failure when encountering distribution shifts due to their reliance on the IID assumption, which is often unfulfilled in practice. They propose a comprehensive review of OOD evaluation, categorizing existing research into three paradigms: OOD performance testing, OOD performance prediction, and OOD intrinsic property characterization. The authors also discuss the challenges of evaluating OOD generalization in the context of pretrained models. By addressing this long-standing problem, the paper aims to provide a foundation for future research in OOD evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about how machine learning models can fail when they encounter new situations that are different from what they were trained on. This is called “out-of-distribution” generalization. The authors want to find out why this happens and how we can measure it. They looked at many existing papers and found three main ways that researchers have tried to solve this problem: testing, predicting, and characterizing the types of distribution shifts a model can handle. They also talked about how these methods work with special kinds of models called “pretrained” models. The authors think that by understanding how OOD generalization works, we can make better machine learning models in the future. |
Keywords
* Artificial intelligence * Generalization * Machine learning
