Few-Shot Anomaly Detection via Personalization
Even with a plenty amount of normal samples, anomaly detection has been considered as a challenging machine learning task due to its one-class nature, i. e., the lack of anomalous samples in training time. It is only recently that a few-shot regime of anomaly detection became feasible in this regard...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10401164/ |