Linganisha mbinu

	GAN Zinazoeleka	Uainishaji wa Picha unaoelezeka
Nyanja	Ujifunzaji wa Kina	Ujifunzaji wa Kina
Familia	Machine learning	Machine learning
Mwaka wa asili	2019 (GAN Dissection); ongoing	2016-2017
Mwanzilishi	Bau, D. et al. (GAN Dissection); broader XAI-GAN community	Selvaraju et al. (Grad-CAM); Ribeiro et al. (LIME)
Aina	Explainable generative model	Post-hoc explainability applied to image classifiers
Chanzo asilia	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).	Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 618-626.
Majina mbadala	XAI-GAN, Interpretable GAN, Transparent GAN, Explainable Generative Model	XAI image classification, interpretable image classifier, explainable CNN, transparent image recognition
Zinazohusiana	4	4
Muhtasari	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.	Explainable image classification combines a deep learning image classifier — typically a CNN or Vision Transformer — with a post-hoc or intrinsic interpretability method such as Grad-CAM, LIME, or SHAP to produce visual or quantitative explanations of why the model assigned a particular label to an image. The goal is to make the classifier's decision process transparent, auditable, and trustworthy.