A training system for training a generative adversarial network, the training system comprising: - an input interface configured to access: - training data for the generative adversarial network, wherein the training data comprises training images - generative model data defining a generative adversarial network comprising a generative part and a discriminative part, wherein the generative part is configured to generate synthetic output images and the discriminative part is configured to discriminate between input images originating from the generative part and the training data, wherein the discriminative part is configured to output a classification and an attention mask (s) identifying parts of an input image which contribute to the classification of the respective input image by the discriminative part, wherein the generative part is configured to receive the attention mask (s) as input and to generate synthesized output images from i) noisy samples (z) drawn from a latent space and ii) the attention mask (s); and - a processor subsystem configured to iteratively train the generative adversarial network based on the training data by establishing an inner iterative loop in the iterative training in which parameters of the generative part are trained by iteratively minimizing a loss function for the generative part, wherein minimizing the loss function comprises maximizing a mutual information term included in the loss function which represents mutual information between: - a current attention mask (s) of the discriminative part, wherein the current attention mask (s) is obtained from the discriminative part by classification of a previous synthesized output image of the generative part which is generated using a previous attention mask (s') obtained from the discriminative part in a previous iteration of the inner iterative loop, and - changes between a current synthesized output image of the generative part which is generated using the current attention mask (s) and the previous synthesized output instance of the generative part. - an input interface configured to access: - training data for the generative adversarial network, wherein the training data comprises training images - generative model data defining a generative adversarial network comprising a generative part and a discriminative part, wherein the generative part is configured to generate synthetic output images and the discriminative part is configured to discriminate between input images originating from the generative part and the training data, wherein the discriminative part is configured to output a classification and an attention mask (s) identifying parts of an input image which contribute to the classification of the respective input image by the discriminative part, wherein the generative part is configured to receive the attention mask (s) as input and to generate synthesized output images from i) noisy samples (z) drawn from a latent space and ii) the attention mask (s); and wherein the discriminative part is configured to output a classification and an attention mask (s) identifying parts of an input image which contribute to the classification of the respective input image by the discriminative part, wherein the generative part is configured to receive the attention mask (s) as input and to generate synthesized output images from i) noisy samples (z) drawn from a latent space and ii) the attention mask (s); and - a processor subsystem configured to iteratively train the generative adversarial network based on the training data by establishing an inner iterative loop in the iterative training in which parameters of the generative part are trained by iteratively minimizing a loss function for the generative part, wherein minimizing the loss function comprises maximizing a mutual information term included in the loss function which represents mutual information between: - a current attention mask (s) of the discriminative part, wherein the current attention mask (s) is obtained from the discriminative part by classification of a previous synthesized output image of the generative part which is generated using a previous attention mask (s') obtained from the discriminative part in a previous iteration of the inner iterative loop, and - changes between a current synthesized output image of the generative part which is generated using the current attention mask (s) and the previous synthesized output instance of the generative part.