Methoden vergleichen

	Erklärbarer Variational Autoencoder	Multimodaler Variational Autoencoder
Fachgebiet	Deep Learning	Deep Learning
Familie	Machine learning	Machine learning
Entstehungsjahr	2013–2017	2018
Urheber	Kingma, D. P. & Welling, M. (VAE); Higgins et al. (beta-VAE for disentanglement)	Wu, M. and Goodman, N.
Typ	Generative model with interpretable latent space	Generative latent-variable model
Wegweisende Quelle	Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).	Wu, M., & Goodman, N. (2018). Multimodal Generative Models for Scalable Weakly-Supervised Learning. Advances in Neural Information Processing Systems (NeurIPS), 31.
Aliasnamen	XVAE, Interpretable VAE, Disentangled Variational Autoencoder, Explainable Generative Model	MVAE, multimodal VAE, multi-modal variational autoencoder, multimodal generative model
Verwandt	4	3
Zusammenfassung	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.	The Multimodal Variational Autoencoder (MVAE) is a deep generative model that learns a shared latent representation across two or more data modalities — such as images and captions — using a product-of-experts fusion of modality-specific encoders, enabling generation and inference even when only a subset of modalities is observed at test time.