Interpretability analysis of image generated by generative adverserial network (gan) model

Abstract

In an embodiment, a GAN model is trained based on an image dataset. A set of images of a first class is generated by the GAN model. Further, a first saliency map of a first generated image is determined by a neural network model. A second saliency map of a second image, belonging to the first class, from image dataset is determined by the neural network model. A first interpretability coefficient is determined, based on the first and second saliency maps. A first typicality score between the first generated image and a first set of images, belonging to the first class, from the image dataset, is determined. A second typicality score between a pair of generated images is determined. A second interpretability coefficient is determined basis the first and second typicality scores. An interpretability score associated with the GAN model is determined based on the first and second interpretability coefficients.