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Found 3 papers, 1 papers with code
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled Data
With our approach, we can approximate the anomaly scores for normal data using the unlabeled and anomaly data.
LogELECTRA: Self-supervised Anomaly Detection for Unstructured Logs
In this paper, we propose LogELECTRA, a new log anomaly detection model that analyzes a single line of log messages more deeply on the basis of self-supervised anomaly detection.
Self-Supervised Anomaly Detection
Supervised Anomaly Detection
ARDIR: Improving Robustness using Knowledge Distillation of Internal Representation
In addition to the output of the teacher model, ARDIR uses the internal representation of the teacher model as a label for adversarial training.
