Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics

• URL: http://arxiv.org/abs/2302.06048v1
• Date: Mon, 13 Feb 2023 01:51:34 GMT
• Title: Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics
• Authors: Minkyung Kim, Junsik Kim, Jongmin Yu, Jun Kyun Choi
• Abstract summary: We propose an unsupervised deep one-class classification that learns normality from pseudo-labeled normal samples. Experiments on 10 anomaly detection benchmarks show that our method effectively improves performance on anomaly detection by sizable margins.
• Score: 11.047949973156836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One-class classification has been a prevailing method in building deep anomaly detection models under the assumption that a dataset consisting of normal samples is available. In practice, however, abnormal samples are often mixed in a training dataset, and they detrimentally affect the training of deep models, which limits their applicability. For robust normality learning of deep practical models, we propose an unsupervised deep one-class classification that learns normality from pseudo-labeled normal samples, i.e., outlier detection in single cluster scenarios. To this end, we propose a pseudo-labeling method by an adaptive threshold selected by ranking-based training dynamics. The experiments on 10 anomaly detection benchmarks show that our method effectively improves performance on anomaly detection by sizable margins.

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