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Anomaly Detection via Multi-Scale Contrasted Memory
Deep anomaly detection (AD) aims to provide robust and efficient classifiers for one-class and unbalanced settings.
Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks
Our model significantly outperforms state-of-the-art with up to 36% relative error improvement on object anomalies and 40% on face anti-spoofing problems.
Fine-grained Anomaly Detection via Multi-task Self-Supervision
Detecting anomalies using deep learning has become a major challenge over the last years, and is becoming increasingly promising in several fields.
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