In the realm of Generative Adversarial Networks (GANs), various techniques have been developed to enhance the quality of generated images. These techniques include:

Style-based GAN (a): This approach focuses on disentangling the high-level attributes of the image, such as pose and identity, from the stochastic variations. By separating these factors, Style-based GANs can generate more diverse and realistic images with finer control over attributes.

Deep Convolutional GAN (DCGAN) (b): DCGANs leverage deep convolutional neural networks to learn hierarchical representations of images. This architecture stabilizes the training process and enables the generation of higher-resolution and more visually appealing images compared to traditional GANs.

Wasserstein GAN (WGAN) (c): WGANs introduce Wasserstein distance as a measure of similarity between the distributions of real and generated images. By optimizing this distance, WGANs facilitate more stable training and produce higher-quality images with improved convergence properties.

These techniques, when applied individually or in combination, contribute to the advancement of GANs in generating high-quality images that exhibit remarkable realism and diversity.

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