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.
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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.
#5 best model for Conditional Image Generation on CIFAR-10
We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature.
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks.
#4 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.
#3 best model for Image Generation on CIFAR-10 (FID metric)
We propose a new equilibrium enforcing method paired with a loss derived from the Wasserstein distance for training auto-encoder based Generative Adversarial Networks.
#12 best model for Image Generation on CIFAR-10
We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout.