Summary of Mano: Exploiting Matrix Norm For Unsupervised Accuracy Estimation Under Distribution Shifts, by Renchunzi Xie et al.
MANO: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts
by Renchunzi Xie, Ambroise Odonnat, Vasilii Feofanov, Weijian Deng, Jianfeng Zhang, Bo An
First submitted to arxiv on: 29 May 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 This paper proposes a new method, MaNo, to estimate the test accuracy of pre-trained neural networks on out-of-distribution (OOD) samples without requiring ground truth labels. By leveraging the models’ outputs, specifically logits, and applying data-dependent normalization, MaNo reduces prediction bias and achieves state-of-the-art performance across various architectures in the presence of shifts. The proposed method is motivated by a study of the relationship between logits and generalization performance, which highlights the connection between the estimation score and the model’s uncertainty. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper helps machines learn better by fixing a common problem with estimating how well they perform on new data without knowing the correct answers. The authors develop a new approach called MaNo that improves upon existing methods by normalizing the model’s output to reduce mistakes and achieve better results. This is important because it can help improve AI systems in real-world applications. |
Keywords
» Artificial intelligence » Generalization » Logits
