Summary of Contrastive Learning Is Not Optimal For Quasiperiodic Time Series, by Adrian Atienza et al.
Contrastive Learning Is Not Optimal for Quasiperiodic Time Series
by Adrian Atienza, Jakob Bardram, Sadasivan Puthusserypady
First submitted to arxiv on: 24 Jul 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 This paper addresses the limitations in Self-Supervised Learning (SSL) for time series analysis, particularly in distinguishing between classes with limited labels. The authors identify the use of Contrastive Learning as a bottleneck, which focuses on encoding unique record-based patterns while neglecting changes across the entire record. To overcome this challenge, they propose Distilled Embedding for Almost-Periodic Time Series (DEAPS), a non-contrastive method tailored for quasiperiodic time series like ECG data. DEAPS integrates a “Gradual Loss (Lgra)” function to guide the model in capturing dynamic patterns evolving throughout the record. The results show a notable improvement of +10% over existing state-of-the-art methods when only a few annotated records are used. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using machines to learn from time series data, like heartbeats or other signals that change over time. Currently, these machines can do well even with just a little information, but they’re not very good at telling different types of patterns apart when there’s only a small amount of labeled data. The problem is that most current methods focus on making sure each piece of data is unique and different from others, which means they miss important changes happening over time. To fix this, the authors introduce a new way to train these machines called DEAPS, which works better for signals like heartbeats. This new method helps the machine learn more about patterns changing over time, and it’s able to do better than other methods when there’s limited labeled data. |
Keywords
» Artificial intelligence » Embedding » Self supervised » Time series
