Shot Image Generation

Few-shot image generation aims to train generative models, primarily GANs and diffusion models, to produce realistic and diverse images from very limited training data (e.g., 10 images). Current research focuses on techniques to mitigate overfitting and improve diversity, including methods that leverage pre-trained models, adapt latent spaces, and incorporate structural and textural information into the generation process. This field is significant because it addresses the data scarcity problem hindering many computer vision applications, enabling the creation of high-quality synthetic images for tasks like data augmentation and novel content generation.