Image generation (synthesis) is the task of generating new images from an existing dataset.
In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.
( Image credit: StyleGAN )
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This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn disentangled representations in a completely unsupervised manner.
#5 best model for Unsupervised MNIST on MNIST
We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.
#8 best model for Conditional Image Generation on CIFAR-10
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks.
#7 best model for Conditional Image Generation on ImageNet 128x128
Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible.
#4 best model for Image Generation on LSUN Bedroom 256 x 256
We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature.
It this paper we revisit the fast stylization method introduced in Ulyanov et.
We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation.