Comparer des méthodes

	GAN Explicable	Réseau antagoniste génératif
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2019 (GAN Dissection); ongoing	2014
Auteur d'origine	Bau, D. et al. (GAN Dissection); broader XAI-GAN community	Goodfellow, I. et al.
Type	Explainable generative model	Generative deep learning (adversarial two-network game)
Source fondatrice	Bau, D., Zhu, J.-Y., Strobelt, H., Zhou, B., Tenenbaum, J. B., Freeman, W. T., & Torralba, A. (2019). GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. In Proceedings of the International Conference on Learning Representations (ICLR 2019).	Goodfellow, I. et al. (2014). Generative Adversarial Nets. NeurIPS.
Alias	XAI-GAN, Interpretable GAN, Transparent GAN, Explainable Generative Model	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
Apparentées	4	4
Résumé	Explainable GAN applies interpretability techniques to Generative Adversarial Networks to reveal which internal units and latent directions cause specific visual or structural features in generated outputs. It combines GAN training with post-hoc analysis tools — such as unit dissection, saliency maps, or disentangled latent spaces — to make generative model behaviour transparent and auditable.	A Generative Adversarial Network (GAN), introduced by Ian Goodfellow and colleagues in 2014, produces realistic synthetic data through the competition of two neural networks — a generator and a discriminator. It is widely used for image synthesis, data augmentation, and distribution estimation.