  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.
