Summary of What Does Softmax Probability Tell Us About Classifiers Ranking Across Diverse Test Conditions?, by Weijie Tu et al.
What Does Softmax Probability Tell Us about Classifiers Ranking Across Diverse Test Conditions?
by Weijie Tu, Weijian Deng, Liang Zheng, Tom Gedeon
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 proposed paper develops a novel measure called Softmax Correlation (SoftmaxCorr) to rank classifier performance on unlabeled out-of-distribution (OOD) data. Building upon conventional uncertainty metrics, SoftmaxCorr calculates the cosine similarity between class-class correlation matrices from softmax output vectors and an ideal reference matrix. This approach assesses model confidence and uniformity across categories, indicating minimal uncertainty and confusion. The measure is evaluated rigorously on ImageNet, CIFAR-10, and WILDS datasets, demonstrating its predictive validity in both in-distribution (ID) and OOD settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to measure how well models work when they’re tested with data that’s different from what they were trained on. They found that a method called Softmax Correlation works really well at predicting how a model will do, even when it sees new types of data. This helps us understand why some models are better than others at dealing with unexpected situations. |
Keywords
» Artificial intelligence » Cosine similarity » Softmax
