قارن الطرق

	المشفر التلقائي المتغير القابل للتفسير	المشفر التلقائي التبايني متعدد الوسائط
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2013–2017	2018
صاحب الطريقة	Kingma, D. P. & Welling, M. (VAE); Higgins et al. (beta-VAE for disentanglement)	Wu, M. and Goodman, N.
النوع	Generative model with interpretable latent space	Generative latent-variable model
المصدر التأسيسي	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.
الأسماء البديلة	XVAE, Interpretable VAE, Disentangled Variational Autoencoder, Explainable Generative Model	MVAE, multimodal VAE, multi-modal variational autoencoder, multimodal generative model
ذات صلة	4	3
الملخص	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.