Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Přenosové učení× | Variační autoenkodér× | |
|---|---|---|
| Obor≠ | Strojové učení | Hluboké učení |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2010 (formalized); 1990s (early roots) | 2014 |
| Tvůrce≠ | Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing) | Kingma, D. P. & Welling, M. |
| Typ≠ | Learning paradigm | Deep generative latent-variable model (encoder–decoder) |
| Původní zdroj≠ | Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359. DOI ↗ | Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link ↗ |
| Další názvy | TL, domain adaptation, fine-tuning, pre-trained model adaptation | Değişkensel Otokodlayıcı (VAE), VAE, auto-encoding variational Bayes, deep latent variable model |
| Příbuzné≠ | 3 | 5 |
| Shrnutí≠ | 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. | The Variational Autoencoder (VAE) is a deep generative latent-variable model, introduced by Diederik Kingma and Max Welling in 2014, that encodes data as a probability distribution in a latent space and samples from that distribution to generate new examples. It is used for data generation, anomaly detection, and feature learning. |
| ScholarGateDatová sada ↗ |
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