Critical Review for One-class Classification: recent advances and the reality behind them
<|reference_start|>Critical Review for One-class Classification: recent advances and the reality behind them: 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.<|reference_end|>
@article{hayashi2024critical,
    title={Critical Review for One-class Classification: recent advances and the
  reality behind them},
    author={Toshitaka Hayashi, Dalibor Cimr, Hamido Fujita, Richard Cimler},
    journal={arXiv preprint arXiv:2404.17931},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.17931},
    primaryClass={cs.LG cs.CV}
}