Infinite Image Generation
1 papers with code • 1 benchmarks • 1 datasets
Infinite Image Generation refers to the task of generating an unlimited number of images that belong to a specific distribution or category. It is a challenging task, as it requires the model to capture the underlying patterns and distributions in the data, and generate images that are diverse, yet still follow the same patterns. There are various techniques and algorithms that can be used to perform infinite image generation, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Convolutional Neural Networks (CNNs).
LHQ
Most implemented papers
Aligning Latent and Image Spaces to Connect the Unconnectable
In this work, we develop a method to generate infinite high-resolution images with diverse and complex content.
