Summary of Logora: Local-global Representation Alignment For Robust Time Series Classification, by Huanyu Zhang et al.
LogoRA: Local-Global Representation Alignment for Robust Time Series Classification
by Huanyu Zhang, Yi-Fan Zhang, Zhang Zhang, Qingsong Wen, Liang Wang
First submitted to arxiv on: 12 Sep 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 A proposed framework, Local-Global Representation Alignment (LogoRA), aims to improve unsupervised domain adaptation of time series data. Existing methods struggle to extract and align global and local features, but LogoRA employs a two-branch encoder with multi-scale convolutional and patching transformer branches to tackle this issue. The framework also includes fusion, invariant feature learning, triplet loss, dynamic time warping-based alignment, adversarial training, and per-class prototype alignment. These strategies enable effective domain-invariant feature alignment from multiple perspectives. LogoRA outperforms strong baselines by up to 12.52% on four time-series datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LogoRA is a new way to help models understand patterns in different types of data over time. This can be helpful when trying to predict what will happen next, even if the data comes from different sources or situations. The problem with current methods is that they don’t do a good job of looking at both big and small details in the data. LogoRA uses two parts to look at these details: one for big patterns and one for small ones. It then combines this information to make predictions. |
Keywords
» Artificial intelligence » Alignment » Domain adaptation » Encoder » Time series » Transformer » Triplet loss » Unsupervised
