Sammenlign metoder

	Explainable Variational Autoencoder	Selv-overvåket Variasjonell Autoenkoder
Fagfelt	Dyp læring	Dyp læring
Familie	Machine learning	Machine learning
Opprinnelsesår	2013–2017	2014 (VAE); self-supervised variant ~2019–2021
Opphavsperson	Kingma, D. P. & Welling, M. (VAE); Higgins et al. (beta-VAE for disentanglement)	Kingma, D. P. & Welling, M. (VAE); self-supervised extensions by various authors from ~2019 onward
Type	Generative model with interpretable latent space	Generative model with self-supervised representation learning
Opprinnelig kilde	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).
Alias	XVAE, Interpretable VAE, Disentangled Variational Autoencoder, Explainable Generative Model	SS-VAE, self-supervised VAE, unsupervised VAE with self-supervised pretext tasks, contrastive VAE
Relaterte	4	6
Sammendrag	An Explainable Variational Autoencoder (XVAE) extends the standard VAE framework with techniques that make its latent space interpretable: disentangling latent dimensions so each corresponds to a human-understandable factor, or post-hoc attribution methods (SHAP, integrated gradients) that trace reconstructions back to input features. It retains the VAE's generative power while adding transparency required in scientific and high-stakes applications.	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.