Summary of Evidentially Calibrated Source-free Time-series Domain Adaptation with Temporal Imputation, by Mohamed Ragab et al.
Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation
by Mohamed Ragab, Peiliang Gong, Emadeldeen Eldele, Wenyu Zhang, Min Wu, Chuan-Sheng Foo, Daoqiang Zhang, Xiaoli Li, Zhenghua Chen
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 approach called MAsk And imPUte (MAPU) for source-free domain adaptation (SFDA) in time series analysis, addressing the challenges of capturing temporal dynamics and preserving the source domain’s privacy. MAPU introduces a temporal imputation task to recover original signals within learned embeddings, bypassing noisy raw data complexities. This method seamlessly integrates with existing SFDA methods, offering greater flexibility. Additionally, the paper introduces E-MAPU, which incorporates evidential uncertainty estimation to address overconfidence issues in softmax predictions. Experimental results on five real-world time series datasets demonstrate significant performance gains for both MAPU and E-MAPU compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to adapt models to work with new data that they haven’t seen before, without having access to the old data. This helps keep the original data private while still allowing the model to learn from it. The old ways of doing this didn’t work well for time series data, which has special patterns and rhythms. The new method, called MAPU, uses a clever trick to figure out what’s missing in the new data and fill it in. This makes the adaptation process much better. Another version of the method, E-MAPU, is even more advanced and can handle situations where the model isn’t entirely sure what it’s doing. |
Keywords
» Artificial intelligence » Domain adaptation » Mask » Softmax » Time series
