Summary of Aetta: Label-free Accuracy Estimation For Test-time Adaptation, by Taeckyung Lee et al.
AETTA: Label-Free Accuracy Estimation for Test-Time Adaptation
by Taeckyung Lee, Sorn Chottananurak, Taesik Gong, Sung-Ju Lee
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes AETTA, a label-free accuracy estimation algorithm for test-time adaptation (TTA) in dynamic scenarios. The authors address the limitations of traditional methods by introducing prediction disagreement as an estimate of accuracy, calculated by comparing model predictions with dropout inferences. They improve this metric to extend its applicability under adaptation failures. The proposed method is evaluated extensively using four baselines and six TTA methods, demonstrating an average improvement of 19.8% over the baselines. The authors also showcase the practicality of their approach through a model recovery case study. AETTA has the potential to improve the reliability of TTA in various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding out how well a machine learning model will work on new, unseen data without needing any labels. It’s like trying to predict which way the model will go wrong or right when it meets new information for the first time. The researchers come up with a new way to do this by looking at how different the model’s predictions are from what it would predict if some parts of its own training process were broken. They test their idea and show that it works really well, especially in situations where the model is likely to go wrong. |
Keywords
» Artificial intelligence » Dropout » Machine learning
