Summary of Discover Your Neighbors: Advanced Stable Test-time Adaptation in Dynamic World, by Qinting Jiang et al.
Discover Your Neighbors: Advanced Stable Test-Time Adaptation in Dynamic World
by Qinting Jiang, Chuyang Ye, Dongyan Wei, Yuan Xue, Jingyan Jiang, Zhi Wang
First submitted to arxiv on: 8 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Multimedia (cs.MM)
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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 research paper tackles a significant challenge in deep learning: how neural networks can adapt to changing environments during testing. Despite advancements, these networks still suffer from decreased quality of experience when facing distribution shifts between training and test domains. The authors identify existing solutions as insufficient for dealing with dynamic test distributions within batches. They propose Discover Your Neighbours (DYN), a novel approach that combines source and test batch normalization statistics to characterize target distributions. DYN consists of layer-wise instance statistics clustering (LISC) and cluster-aware batch normalization (CABN). LISC clusters samples based on cosine similarity, while CABN aggregates statistics for robust representations. The authors demonstrate the effectiveness of DYN in maintaining performance under dynamic data stream patterns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper helps deep learning networks adapt to changing environments during testing. When these networks face different conditions between training and test phases, their quality of experience drops. To solve this problem, the researchers propose a new approach called Discover Your Neighbours (DYN). DYN uses special techniques to combine information from the source data and the test data to create more accurate representations. This makes it better at handling changes in the data during testing. The authors show that DYN is effective in maintaining performance under changing conditions. |
Keywords
» Artificial intelligence » Batch normalization » Clustering » Cosine similarity » Deep learning
