قارن الطرق

	نموذج الانتشار القابل للتفسير	المشفر التلقائي المتغير القابل للتفسير
المجال	التعلم العميق	التعلم العميق
العائلة	Machine learning	Machine learning
سنة النشأة	2020–2022	2013–2017
صاحب الطريقة	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)
النوع	Generative model with post-hoc or intrinsic explainability	Generative model with interpretable latent space
المصدر التأسيسي	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).
الأسماء البديلة	XAI-DDPM, interpretable diffusion model, transparent diffusion model, explainable DDPM	XVAE, Interpretable VAE, Disentangled Variational Autoencoder, Explainable Generative Model
ذات صلة	6	4
الملخص	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.