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| Selv-overvåget autoencoder anomalidetektion× | One-Class SVM× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2018–2020 | 1999–2001 |
| Ophavsperson≠ | Golan & El-Yaniv; broader self-supervised anomaly detection community | Scholkopf, B., Platt, J. C., Smola, A. J., Williamson, R. C. |
| Type≠ | Unsupervised / self-supervised deep learning | Anomaly / novelty detection (unsupervised) |
| Oprindelig kilde≠ | Golan, I. & El-Yaniv, R. (2018). Deep one-class classification via geometric transformations. Advances in Neural Information Processing Systems (NeurIPS), 31. link ↗ | Scholkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471. DOI ↗ |
| Aliasser | SSL Autoencoder anomaly detection, self-supervised reconstruction anomaly detection, pretext-task autoencoder anomaly detection, contrastive autoencoder anomaly detection | OCSVM, one-class support vector machine, novelty SVM, unsupervised SVM |
| Relaterede≠ | 6 | 3 |
| Resumé≠ | Self-supervised autoencoder anomaly detection trains an autoencoder using self-supervised pretext tasks — such as predicting geometric transformations or solving jigsaw puzzles — on unlabeled normal data, then flags as anomalous any input whose reconstruction error or pretext-task score deviates substantially from the learned normal distribution. | One-class SVM is an unsupervised anomaly and novelty detection algorithm that learns a tight boundary around normal training data in a kernel-induced feature space, flagging new observations that fall outside that boundary as outliers. Introduced by Scholkopf et al. in 1999–2001, it extends the SVM framework to the single-class setting where no labelled anomalies are available. |
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