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）是异常检测的经典方法。借助预训练骨干网络的强大表示能力，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网络特征的判别性，从而获得显著效果。尽管当前方法侧重于特征迁移策略，本文指出OCC方法中的优化目标空间也可能是影响性能的关键潜在因素。本研究对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，我们揭示了一个核心观点：任意具有合适范数的空间均可等价替代超球面中心，且无需依赖训练样本的分布假设。进一步，我们给出了OCC优化目标中范数可行域的确定准则。这一新见解催生了一种简单且与数据无关的深度单类分类方法。该方法直接明了，仅使用单个1×1卷积层作为可训练投影器，并以任意具有合适范数的空间作为优化目标。大量实验验证了本文发现及相关方法的可靠性与有效性，在单类分类任务、工业视觉异常检测与分割任务中均取得了最先进的性能。