###### Abstract
The loss function, however, was the Jensen-Shannon divergence [5], which can lead to mode collapse and vanishing gradients [8; 11]. The WGAN-GP [12] addresses these problems by using the Wasserstein distance as a loss function, which provides a more meaningful measure of the difference between the probability distributions. WGAN-GP also introduces a gradient penalty term that penalizes the norm of \(D\)'s gradients, encouraging more diverse generated samples by enforcing a more uniform distribution of gradients throughout the data space. The RAGAN [13] introduces the relativistic discriminator that estimates the probability that an authentic sample is more realistic than a fake sample and vice versa. It considers the relative realness of real and fake samples, providing more informative feedback to \(G\). RAGAN delivers a more nuanced signal to \(G\), allowing it to understand better how to generate plausible samples on its own and the actual data distribution. Recent techniques have focused on improving the generator's training rather than the discriminator. The approaches involve providing more information about the images' features to \(G\) and making the competition between \(G\) and \(D\) more balanced. For instance, [PERSON] et al. [9] proposed a training technique that raises the spatial awareness of \(G\). The strategy consists of sampling multi-level heatmaps from \(D\) using Grad-CAM and integrating them into the feature maps of \(G\) via the spatial encoding layer. The authors used \(D\) as a regularizer, aligning the spatial awareness of \(G\) with \(D\)'s attention maps. [PERSON] et al. [10] argue that since \(G\)'s weights are updated only with gradients derived from \(D\), \(D\) acts as a referee rather than a player. The authors propose a new training approach with a generator-leading task to make the adversarial game fairer. In this task, \(D\) must extract features \(G\) can decode to reconstruct the input. Another possible solution is to combine GANs with explainable artificial intelligence (XAI) in the loss function. In a classification task, XAI generates explanations indicatingwhich parts of the input were considered the most critical in assigning the input to a label. Moreover, it is possible to use the model's gradients to derive these explanations [14]. A widely used method is Saliency [15], which creates explanations by calculating the gradients of the output concerning the input features. It allows for highlighting of regions where a slight change in the input would significantly change the prediction. However, this approach can produce noisy explanations. The Gradient\(\odot\)Input [16] addresses this issue by calculating the elementwise multiplication of gradients by the input. The input acts as a model-independent filter, which reduces noise and smooths the explanations. Another widely used method is DeepLIFT [16], which attributes importance to the input features by comparing the activations that the actual input and a reference input cause in each neuron. It uses the difference between the activations as importance scores for the input features. Gradient-based XAI generates detailed, fine-grained explanations at the pixel level with significant computational efficiency compared to XAI based on feature perturbation [17; 18]. The images generated by a combination of GAN and XAI in the loss function can be used in a data augmentation task and potentially improve the classification performance of state-of-the-art methods, such as Transformer-based models [19; 20; 21; 22]. These methods leverage self-attention mechanisms to enable holistic image understanding and achieve top-notch performance in visual recognition tasks [23; 24; 25; 26; 27; 28; 29]. ViT [19] is a prime example. It operates on a patch-based representation of images using the self-attention mechanism to capture global dependencies and learn long-range relationships between the patches.