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.

翻译：单类分类（OCC）是异常检测中一种长期使用的方法。借助预训练骨干强大的表征能力，OCC方法在性能上取得了显著提升。通常，大多数OCC方法采用迁移学习来增强预训练骨干特征的判别性，从而获得卓越效果。尽管当前研究多聚焦于特征迁移策略，我们认为OCC方法中的优化目标空间也可能是影响性能的关键因素。本文对OCC的优化目标进行了深入探究。通过严谨的理论分析与推导，我们揭示了一个关键见解：任何具有适当范数的空间均可作为超球面中心的等价替代，且无需依赖训练样本的分布假设。此外，我们提出了确定OCC优化目标中范数可行域的具体准则。这一新颖见解催生了一种简单且对数据无偏的深度单类分类方法。该方法直观简洁：仅需一个1×1卷积层作为可训练投影器，并以任意具有适当范数的空间作为优化目标。大量实验验证了本发现及相关方法的可靠性与有效性，并在单类分类、工业视觉异常检测与分割任务中取得了最先进的性能。