手法を比較

	説明可能なGAN	Generative Adversarial Network
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2019 (GAN Dissection); ongoing	2014
提唱者	Bau, D. et al. (GAN Dissection); broader XAI-GAN community	Goodfellow, I. et al.
種類	Explainable generative model	Generative deep learning (adversarial two-network game)
原典	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.
別名	XAI-GAN, Interpretable GAN, Transparent GAN, Explainable Generative Model	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
関連	4	4
概要	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.