Порівняння методів
Переглядайте обрані методи поруч; рядки з відмінностями підсвічено.
| Самокероване навчання× | Трансферне навчання× | |
|---|---|---|
| Галузь | Машинне навчання | Машинне навчання |
| Родина | Machine learning | Machine learning |
| Рік появи≠ | 2018–2020 | 2010 (formalized); 1990s (early roots) |
| Автор методу≠ | LeCun, Y. and community (formalized ~2018–2020) | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) |
| Тип≠ | Representation learning paradigm | Learning paradigm |
| Основоположне джерело≠ | 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 ↗ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ |
| Інші назви | SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning | TL, domain adaptation, fine-tuning, pre-trained model adaptation |
| Пов'язані | 3 | 3 |
| Підсумок≠ | 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. | Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond. |
| ScholarGateНабір даних ↗ |
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