পদ্ধতির তুলনা করুন

	ব্যাখ্যাযোগ্য GAN	জেনারেটিভ অ্যাডভারসারিয়াল নেটওয়ার্ক
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2019 (GAN Dissection); ongoing	2014
প্রবর্তক	Bau, D. et al. (GAN Dissection); broader XAI-GAN community	Goodfellow, I. et al.
ধরন	Explainable generative model	Generative deep learning (adversarial two-network game)
মৌলিক উৎস	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.
অপর নাম	XAI-GAN, Interpretable GAN, Transparent GAN, Explainable Generative Model	Üretici Çekişmeli Ağ (GAN), GAN, generative adversarial nets, adversarial network
সম্পর্কিত	4	4
সারসংক্ষেপ	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.