Summary of Gradient Alignment with Prototype Feature For Fully Test-time Adaptation, by Juhyeon Shin and Jonghyun Lee and Saehyung Lee and Minjun Park and Dongjun Lee and Uiwon Hwang and Sungroh Yoon
Gradient Alignment with Prototype Feature for Fully Test-time Adaptation
by Juhyeon Shin, Jonghyun Lee, Saehyung Lee, Minjun Park, Dongjun Lee, Uiwon Hwang, Sungroh Yoon
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 novel regularizer for Test-time Adaptation (TTA) called Gradient Alignment with Prototype feature (GAP). The GAP regularizer addresses the issue of misclassified pseudo-labels influencing the adaptation process. To achieve this, the authors develop a gradient alignment loss that precisely manages changes made to the model without negatively impacting its performance on other data. A prototype feature is introduced as a proxy measure of the negative impact, and the formula is tailored for feasibility under TTA constraints where only test data without labels are available. The paper demonstrates GAP’s effectiveness in improving TTA methods across various datasets, showcasing its versatility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better adapt our models to new situations without needing more labeled training data. Right now, when we try to use models in new situations, they can get confused and not perform well because they were trained on different kinds of data. The authors of this paper are trying to solve this problem by creating a special kind of “regularizer” that helps the model learn from its mistakes and avoid getting worse. They’re doing this by comparing how well the model is performing on different types of data and adjusting its behavior accordingly. This could be very helpful for things like self-driving cars or medical diagnosis, where we need models to work well in new situations without having to retrain them. |
Keywords
* Artificial intelligence * Alignment
