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

Autor:	Kim, Minkyung, Kim, Junsik, Yu, Jongmin, Choi, Jun Kyun
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1109/ICDMW58026.2022.00014
Popis:	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. Comment: 8 pages, 6 figures, 2022 IEEE International Conference on Data Mining Workshops (ICDMW)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2302.06048 Zobrazit plný text záznamu View this record from Arxiv