Infinite Image Generation

Infinite image generation refers to the task in the computer vision field of generating an infinite number of images that belong to a specific distribution or category through models. The goal is to enable the model to capture the underlying patterns and distributions in the data and generate images that are both diverse and consistent with the same patterns. Infinite image generation has significant application value, such as data augmentation, artistic creation, and virtual reality, among others. Common techniques include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Convolutional Neural Networks (CNNs).