## 3 Advancements in Generative Adversarial Network Research
### Supervised Generative Adversarial Networks  
Supervised GANs, on the other hand, are trained on labeled data and learn to generate samplings that match the given labels. The generator and discriminator are both conditioned on the class labels during training. In [8], the authors concentrate on automating sexual facial expression recognition (SFER) to identify sexual expressions as macro facial expressions. Their aim is to detect exotic content in images, particularly aiding in cases of child sexual exploitation material (CSEM) where only facial images are available. They extend the conditional generative adversarial networks (CGANs) to auxiliary classifier GANs (AC-GANs) for controlled image generation. The research introduces the Triple-BigGAN model to enhance facial expression recognition through conditional image synthesis. They introduce a new dataset, SEA-Faces-30k, due to the scarcity of SFER datasets. The experimental results show the Triple-BigGAN's ability to generate high-quality images that enhance the error rates of supervised-learning-based methods. Its performance is evaluated using FID and IS scores, surpassing state-of-the-art methods with 93.59% accuracy. Additionally, the performance of Triple-BigGAN is compared with inference-based GANs on the MNIST, CIFAR-10, and SVHN datasets, achieving superior results. Ref. [5] validates the significance of FID over IS as an evaluation index for GANs. However, directly using FID presents challenges due to resource-intensive time and memory requirements. Ref. [92] discusses a framework named InterFaceGAN that aims to comprehend and interpret the untangled face representation learned by GANs in face synthesis. The authors study the properties of the facial semantics encoded in the latent space of the GANs and find that the GANs learn diverse facial attributes in some linear subspaces of the latent space. They also manage to manipulate these attributes, including gender, age, expression, and eyeglasses, and pose and fix artifacts in a photo-realistic way. The authors perform a detailed analysis of the relationship between attributes and editing results, applying the InterFaceGAN approach to the editing of real faces. The results indicate that GANs learn a controllable and disentangled face representation through face synthesis. They also explore the understanding of GANs and how to realistically manipulate facial attributes. However, they highlight the need to investigate the interpretation of GANs on more general objectsand scenarios and to improve methods for unsupervised learning of semantics. Their work on InterFaceGAN represents an advance towards a broader interpretation of GAN models. The research discussed in [69] reveals that, despite an optimal set of generator parameters, GAN training often fails to discover them. This raises the question of whether non-adversarial generative models should be considered as potential replacements for GANs. Experimental findings indicate that training a combination of GANs yields more promising outcomes than raising the complexity of standalone networks that are already adequately complex for modeling multi-modal data. Moreover, current GAN models such as BigGAN, CR-BigGAN, and LOGAN require significant computational resources, utilizing numerous TPU cores that match the image height or width. As a result, many researchers need more resources to conduct large-scale experiments of this nature. However, with increased computing power, it is anticipated that large mixtures of GANs could be trained on datasets like ImageNet \(128\times 128\), leading to further advancements in the field. Ref. [93] proposes a new hybrid generative model that merges an AE and a GAN called dual distribution matching GAN (DDMGAN). The authors aim to address issues with GANs, such as training flux and mode collapse, by performing distribution matching in the data and latent representation spaces. The low-dimensional latent representation space is obtained through training an AE.