Confronta i metodi

	Modello di Diffusione Spiegabile	Autoencoder Variazionale Spiegabile
Campo	Apprendimento profondo	Apprendimento profondo
Famiglia	Machine learning	Machine learning
Anno di origine	2020–2022	2013–2017
Ideatore	Ho, J., Jain, A., & Abbeel, P. (DDPM, 2020); XAI augmentation by subsequent researchers	Kingma, D. P. & Welling, M. (VAE); Higgins et al. (beta-VAE for disentanglement)
Tipo	Generative model with post-hoc or intrinsic explainability	Generative model with interpretable latent space
Fonte seminale	Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems, 33, 6840–6851.	Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. In Proceedings of the 2nd International Conference on Learning Representations (ICLR 2014).
Alias	XAI-DDPM, interpretable diffusion model, transparent diffusion model, explainable DDPM	XVAE, Interpretable VAE, Disentangled Variational Autoencoder, Explainable Generative Model
Correlati	6	4
Sintesi	An Explainable Diffusion Model couples a denoising diffusion probabilistic model with post-hoc or intrinsic explainability techniques — such as SHAP, gradient-based saliency, attention analysis, or concept-based probing — so that each generative or predictive decision can be audited and justified rather than treated as a black box.	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.