This paper offers a comprehensive review of one-class classification (OCC),
examining the technologies and methodologies employed in its implementation. It
delves into various approaches utilized for OCC across diverse data types, such
as feature data, image, video, time series, and others. Through a systematic
review, this paper synthesizes promi-nent strategies used in OCC from its
inception to its current advance-ments, with a particular emphasis on the
promising application. Moreo-ver, the article criticizes the state-of-the-art
(SOTA) image anomaly de-tection (AD) algorithms dominating one-class
experiments. These algo-rithms include outlier exposure (binary classification)
and pretrained model (multi-class classification), conflicting with the
fundamental con-cept of learning from one class. Our investigation reveals that
the top nine algorithms for one-class CIFAR10 benchmark are not OCC. We ar-gue
that binary/multi-class classification algorithms should not be com-pared with
OCC.

对一类分类（OCC）进行全面回顾，检查其实现中所采用的技术和方法，探讨了在不同数据类型（如特征数据、图像、视频、时间序列等）中用于 OCC 的各种方法。通过系统回顾，综合总结了自 OCC 起始到目前的主要策略，重点关注了有前景的应用。此外，该文章批评了主导一类实验的最新图像异常检测（AD）算法，如离群值暴露（二分类）和预训练模型（多分类），与从单一类别中学习的基本概念相冲突。我们的调查表明，用于一类 CIFAR10 基准测试的前九个算法并非 OCC。我们认为二分类 / 多分类算法不应该与 OCC 进行比较。