Summary of Dacad: Domain Adaptation Contrastive Learning For Anomaly Detection in Multivariate Time Series, by Zahra Zamanzadeh Darban et al.
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
by Zahra Zamanzadeh Darban, Yiyuan Yang, Geoffrey I. Webb, Charu C. Aggarwal, Qingsong Wen, Mahsa Salehi
First submitted to arxiv on: 17 Apr 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 model for time series anomaly detection called Domain Adaptation Contrastive learning model for Anomaly Detection (DACAD). The scarcity of labeled data is a significant challenge in developing accurate models. DACAD combines unsupervised domain adaptation with contrastive learning to detect anomalies in an unlabeled target domain. It utilizes an anomaly injection mechanism that enhances generalization across unseen anomalous classes, improving adaptability and robustness. The model also employs supervised contrastive loss for the source domain and self-supervised contrastive triplet loss for the target domain. A Centre-based Entropy Classifier (CEC) is used to accurately learn normal boundaries in the source domain. DACAD outperforms existing models on multiple real-world datasets, demonstrating its ability to transfer knowledge across domains and mitigate the challenge of limited labeled data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a big problem in detecting unusual patterns in time series data. Right now, it’s hard to train good models because there aren’t enough examples of what normal and abnormal data look like. The authors came up with a new way to use some existing knowledge to make their model better at finding anomalies. They call this approach Domain Adaptation Contrastive learning model for Anomaly Detection (DACAD). DACAD is good at learning patterns in the normal data and then using that to find unusual patterns elsewhere. It even gets better at doing this as it sees more examples of abnormal data. This could be really helpful in many different fields where people need to detect unusual events or patterns. |
Keywords
» Artificial intelligence » Anomaly detection » Contrastive loss » Domain adaptation » Generalization » Self supervised » Supervised » Time series » Triplet loss » Unsupervised
