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.
#2 best model for Image Generation on CUB 128 x 128
We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.
#9 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
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.
In many domains of computer vision, generative adversarial networks (GANs) have achieved great success, among which the fam- ily of Wasserstein GANs (WGANs) is considered to be state-of-the-art due to the theoretical contributions and competitive qualitative performance.