arXiv:2505.03509v3 Announce Type: replace Abstract: Anomaly detection in large datasets is essential in astronomy and computer vision. However, due to a scarcity of labelled data, it is often infeasible to apply supervised methods to anomaly detection. We present AnomalyMatch, an anomaly detection framework combining the semi-supervised FixMatch algorithm using EfficientNet classifiers with active learning. AnomalyMatch is tailored for large-scale applications and integrated into the ESA Datalabs science platform. In this method, we treat anomaly detection as a binary classification problem an
The proliferation of massive datasets in fields like astronomy and computer vision necessitates advanced AI methods for anomaly detection, particularly given the scarcity of labeled data for traditional supervised learning.
This development allows for the efficient discovery of rare but critical events or objects in large datasets, enhancing scientific discovery and operational efficiency across various domains.
The ability to semi-supervise and actively learn in anomaly detection drastically reduces the reliance on extensive human labeling, making large-scale anomaly discovery more feasible and scalable.
- · Astronomy research institutions
- · Computer vision companies
- · Data scientists
- · AI platform providers
- · Manual data labeling services (for anomaly detection)
- · Traditional statistical anomaly detection methods
Increased rate of discovery of novel phenomena in scientific datasets and improved system monitoring for anomalies.
Accelerated progress in fields heavily reliant on big data analysis, leading to new scientific insights and technological breakthroughs.
Potential for autonomous systems to prioritize and flag critical data points for human intervention, creating more efficient human-AI collaboration models.
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