Single GAN

Single GANs are generative adversarial networks trained on a limited dataset, often a single image or a small set of images, aiming to generate diverse and realistic samples from that limited input. Current research focuses on improving the diversity and fidelity of generated samples, exploring architectures that leverage morphological operations, efficient latent space manipulation, and incorporating auxiliary classifiers to enhance control and data efficiency. This research is significant because it addresses the limitations of traditional GANs requiring massive datasets, enabling applications in scenarios with scarce data, such as medical imaging and personalized content generation.