Summary of Protected Test-time Adaptation Via Online Entropy Matching: a Betting Approach, by Yarin Bar et al.
Protected Test-Time Adaptation via Online Entropy Matching: A Betting Approach
by Yarin Bar, Shalev Shaer, Yaniv Romano
First submitted to arxiv on: 14 Aug 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 The proposed paper introduces a novel approach to test-time adaptation via online self-training, which detects distribution shifts using a statistical framework and adapts the classifier’s parameters accordingly. The method departs from conventional self-training by focusing on minimizing entropy values. Instead, it uses concepts from betting martingales and online learning to detect distribution shifts and update the classifier’s parameters. This approach is shown to improve test-time accuracy under distribution shifts while maintaining accuracy and calibration in their absence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to help machine learning models adapt to changing conditions during testing. It uses special math ideas to figure out when the model’s predictions are getting weird, which means it needs to adjust itself. This is different from usual self-training methods that try to make the model more accurate by making it predict things correctly. The paper shows how its new method works and compares it to other ways of doing things. It finds that its approach does better in situations where the data changes a lot. |
Keywords
» Artificial intelligence » Machine learning » Online learning » Self training
