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| Self-supervised One-class SVM× | Selbstüberwachtes Lernen× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2018 | 2018–2020 |
| Urheber≠ | Golan & El-Yaniv; Ruff et al. | LeCun, Y. and community (formalized ~2018–2020) |
| Typ≠ | Self-supervised anomaly/novelty detection | Representation learning paradigm |
| Wegweisende Quelle≠ | Golan, I. & El-Yaniv, R. (2018). Deep One-Class Classification. Proceedings of the 35th International Conference on Machine Learning (ICML), PMLR 80, 1747–1756. link ↗ | LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/ link ↗ |
| Aliasnamen | SS-OCSVM, Self-supervised SVDD, Self-supervised novelty detection, Pretext-task OC-SVM | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning |
| Verwandt≠ | 6 | 3 |
| Zusammenfassung≠ | Self-supervised One-class SVM combines pretext-task-based representation learning with One-class SVM to detect anomalies and novelties without requiring labeled anomaly examples. The model first learns expressive feature embeddings from normal data alone, then fits an OC-SVM boundary in the learned feature space to flag out-of-distribution samples. | Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples. |
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