Porovnat metody

	Samo-dohledový variační autoenkodér	Jemně doladěný variační autoenkodér
Obor	Hluboké učení	Hluboké učení
Rodina	Machine learning	Machine learning
Rok vzniku	2014 (VAE); self-supervised variant ~2019–2021	2014 (VAE); fine-tuning practice from 2015 onward
Tvůrce	Kingma, D. P. & Welling, M. (VAE); self-supervised extensions by various authors from ~2019 onward	Kingma, D. P. & Welling, M. (VAE); fine-tuning strategy from transfer learning literature
Typ	Generative model with self-supervised representation learning	Generative model with fine-tuning
Původní zdroj	Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).	Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).
Další názvy	SS-VAE, self-supervised VAE, unsupervised VAE with self-supervised pretext tasks, contrastive VAE	fine-tuned VAE, domain-adapted VAE, transfer-learned VAE, adapted variational autoencoder
Příbuzné	6	6
Shrnutí	A Self-supervised Variational Autoencoder (SS-VAE) combines the generative latent-space learning of a standard VAE with self-supervised pretext tasks — such as contrastive augmentation, masked reconstruction, or rotation prediction — to learn richer, more disentangled representations from unlabeled data without any manual annotation.	A Fine-Tuned Variational Autoencoder begins with a VAE pre-trained on a large source dataset and then continues training on a smaller target-domain dataset. This approach adapts the learned latent representation and generative capacity to new data, preserving general structure while specializing to the target distribution — yielding better results than training from scratch when labeled or large target data is scarce.