Summary of Temporal Source Recovery For Time-series Source-free Unsupervised Domain Adaptation, by Yucheng Wang et al.
Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptation
by Yucheng Wang, Peiliang Gong, Min Wu, Felix Ott, Xiaoli Li, Lihua Xie, Zhenghua Chen
First submitted to arxiv on: 29 Sep 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 A novel approach to Time-Series Source-Free Unsupervised Domain Adaptation (TS-SFUDA) is proposed, addressing the challenge of transferring temporal dependencies across domains. The Temporal Source Recovery (TemSR) framework transfers temporal dependencies without requiring source-specific designs. It features a recovery process that leverages masking, recovery, and optimization to generate a source-like distribution with recovered source temporal dependencies. Segment-based regularization is designed to restore local dependencies, while anchor-based recovery diversity maximization enhances the diversity of the source-like distribution. The source-like distribution is then adapted to the target domain using traditional UDA techniques. Experimental results demonstrate the effectiveness of TemSR, even surpassing existing TS-SFUDA methods that require source domain designs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help computers adapt to different types of time-series data without needing access to all the original data is developed. This approach, called Temporal Source Recovery (TemSR), helps computers learn from one type of data and apply it to another type. TemSR uses special techniques to create a fake version of the original data that has the same patterns and features as the real thing. It then uses this fake data to train a computer model to perform well on the new type of data. Tests show that TemSR works better than other methods that require access to all the original data. |
Keywords
» Artificial intelligence » Domain adaptation » Optimization » Regularization » Time series » Unsupervised
