Summary of Datta: Towards Diversity Adaptive Test-time Adaptation in Dynamic Wild World, by Chuyang Ye et al.
DATTA: Towards Diversity Adaptive Test-Time Adaptation in Dynamic Wild World
by Chuyang Ye, Dongyan Wei, Zhendong Liu, Yuanyi Pang, Yixi Lin, Jiarong Liao, Qinting Jiang, Xianghua Fu, Qing Li, Jingyan Jiang
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Diversity Adaptive Test-Time Adaptation (DATTA) method aims to improve the Quality of Experience (QoE) by dynamically selecting the best batch normalization methods and fine-tuning strategies based on the Diversity Score. The approach features three key components: Diversity Discrimination (DD) to assess batch diversity, Diversity Adaptive Batch Normalization (DABN) to tailor normalization methods, and Diversity Adaptive Fine-Tuning (DAFT) to selectively fine-tune the model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This method is designed to address distribution shifts between training and testing data by adjusting models on test samples. The paper proposes a new general approach that dynamically selects the best batch normalization methods and fine-tuning strategies based on the Diversity Score, which differentiates between high and low diversity score batches. This results in up to a 21% increase in accuracy compared to state-of-the-art methodologies. |
Keywords
» Artificial intelligence » Batch normalization » Fine tuning
