Bandingkan metode

	GAN yang Dapat Dijelaskan	Jaringan Adversarial Generatif
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2019 (GAN Dissection); ongoing	2014
Pencetus	Bau, D. et al. (GAN Dissection); broader XAI-GAN community	Goodfellow, I. et al.
Tipe	Explainable generative model	Generative deep learning (adversarial two-network game)
Sumber perintis	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
Terkait	4	4
Ringkasan	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.