Exploring the Optimization Objective of One-Class Classification for Anomaly Detection

• URL: http://arxiv.org/abs/2308.11898v2
• Date: Fri, 25 Aug 2023 06:38:23 GMT
• Title: Exploring the Optimization Objective of One-Class Classification for Anomaly Detection
• Authors: Han Gao, Huiyuan Luo, Fei Shen, Zhengtao Zhang
• Abstract summary: One-class classification (OCC) is a longstanding method for anomaly detection. In this work, we conduct a thorough investigation into the optimization objective of OCC. We unveil a key insights: any space with the suitable norm can serve as an equivalent substitute for the hypersphere center. This novel insight sparks a simple and data-agnostic deep one-class classification method.
• Score: 2.9266769103356305
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One-class classification (OCC) is a longstanding method for anomaly detection. With the powerful representation capability of the pre-trained backbone, OCC methods have witnessed significant performance improvements. Typically, most of these OCC methods employ transfer learning to enhance the discriminative nature of the pre-trained backbone's features, thus achieving remarkable efficacy. While most current approaches emphasize feature transfer strategies, we argue that the optimization objective space within OCC methods could also be an underlying critical factor influencing performance. In this work, we conducted a thorough investigation into the optimization objective of OCC. Through rigorous theoretical analysis and derivation, we unveil a key insights: any space with the suitable norm can serve as an equivalent substitute for the hypersphere center, without relying on the distribution assumption of training samples. Further, we provide guidelines for determining the feasible domain of norms for the OCC optimization objective. This novel insight sparks a simple and data-agnostic deep one-class classification method. Our method is straightforward, with a single 1x1 convolutional layer as a trainable projector and any space with suitable norm as the optimization objective. Extensive experiments validate the reliability and efficacy of our findings and the corresponding methodology, resulting in state-of-the-art performance in both one-class classification and industrial vision anomaly detection and segmentation tasks.

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