Summary of Critical Review For One-class Classification: Recent Advances and the Reality Behind Them, by Toshitaka Hayashi et al.
Critical Review for One-class Classification: recent advances and the reality behind them
by Toshitaka Hayashi, Dalibor Cimr, Hamido Fujita, Richard Cimler
First submitted to arxiv on: 27 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a comprehensive review of one-class classification (OCC), exploring various methodologies and technologies employed in its implementation across different data types, including feature data, images, videos, time series, and others. The systematic review synthesizes prominent strategies used in OCC from its inception to current advancements, with a focus on the promising applications. Notably, the paper criticizes state-of-the-art (SOTA) image anomaly detection (AD) algorithms dominating one-class experiments, arguing that these algorithms conflict with the fundamental concept of learning from one class. The investigation reveals that top-performing algorithms for the one-class CIFAR10 benchmark are not OCC, emphasizing the need to distinguish between binary/multi-class classification and OCC approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a type of artificial intelligence called “one-class classification”. It shows how different techniques have been used to make decisions based on just one type of data. The review highlights some successes and failures in this area, especially with image recognition. Some experts say that the way people are comparing these algorithms is wrong because it’s not what the technology was designed for. They found that the top-performing algorithms aren’t actually doing one-class classification. |
Keywords
» Artificial intelligence » Anomaly detection » Classification » Time series
