Summary of The Best Of Both Worlds: on the Dilemma Of Out-of-distribution Detection, by Qingyang Zhang et al.
The Best of Both Worlds: On the Dilemma of Out-of-distribution Detection
by Qingyang Zhang, Qiuxuan Feng, Joey Tianyi Zhou, Yatao Bian, Qinghua Hu, Changqing Zhang
First submitted to arxiv on: 12 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 A novel approach to out-of-distribution (OOD) detection is introduced, which aims to simultaneously identify semantic OOD samples and robustly generalize for covariate-shifted OOD samples. State-of-the-art methods are found to sacrifice OOD generalization ability in favor of superior OOD detection performance, leading to poor classification accuracy in the presence of minor noise. The authors theoretically demystify this “sensitive-robust” dilemma and propose a theory-inspired algorithm that decouples uncertainty learning from a Bayesian perspective. This approach harmonizes the conflict between OOD detection and generalization, achieving dual-optimal performance on standard benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary OOD detection is important for trusting AI models. A new method is developed to improve out-of-distribution detection while also being good at generalizing to new situations. Most existing methods do a great job of detecting unusual samples but struggle when the input data changes slightly. The authors find that this is because those methods sacrifice their ability to generalize well in order to be better at detection. They then develop an algorithm that solves this problem by separating uncertainty learning from a Bayesian perspective. This allows for both good OOD detection and generalization performance on standard benchmarks. |
Keywords
» Artificial intelligence » Classification » Generalization
