Summary of Mita: Bridging the Gap Between Model and Data For Test-time Adaptation, by Yige Yuan et al.
MITA: Bridging the Gap between Model and Data for Test-time Adaptation
by Yige Yuan, Bingbing Xu, Teng Xiao, Liang Hou, Fei Sun, Huawei Shen, Xueqi Cheng
First submitted to arxiv on: 12 Oct 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 Meet-In-The-Middle based Test-Time Adaptation (MITA) model addresses the limitations of existing TTA methods by introducing energy-based optimization. This approach encourages mutual adaptation between the model and data from opposing directions, allowing for a more effective bridging of the gap between the model’s distribution and data characteristics. MITA is demonstrated to outperform state-of-the-art (SOTA) methods in comprehensive experiments across three distinct scenarios: outlier detection, mixture distributions, and pure data cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MITA is a new way to help models work better with real-world data by adapting to individual instances. This helps the model learn from both patterns and unique characteristics of each piece of data. In tests, MITA did better than other methods in situations where there are outliers or mixed distributions. |
Keywords
» Artificial intelligence » Optimization » Outlier detection
