Computer Vision
 
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Image Generation

1662 papers with code • 86 benchmarks • 65 datasets

Image Generation (synthesis) is the task of generating new images from an existing dataset.

  • Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
  • Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.

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 )

Libraries

Use these libraries to find Image Generation models and implementations

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

Analyzing and Improving the Image Quality of StyleGAN

NVlabs/stylegan2 • • CVPR 2020

Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.

121
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Wasserstein GAN

labmlai/annotated_deep_learning_paper_implementations • • 26 Jan 2017

We introduce a new algorithm named WGAN, an alternative to traditional GAN training.

118
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Improved Training of Wasserstein GANs

igul222/improved_wgan_training • • NeurIPS 2017

Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability.

110
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Progressive Growing of GANs for Improved Quality, Stability, and Variation

tkarras/progressive_growing_of_gans • • ICLR 2018

We describe a new training methodology for generative adversarial networks.

105
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A Style-Based Generator Architecture for Generative Adversarial Networks

NVlabs/stylegan • • CVPR 2019

We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature.

83
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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

bioinf-jku/TTUR • • NeurIPS 2017

Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible.

63
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Denoising Diffusion Probabilistic Models

hojonathanho/diffusion • • NeurIPS 2020

We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics.

57
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Self-Attention Generative Adversarial Networks

brain-research/self-attention-gan • • arXiv 2018

In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks.

49
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Improved Techniques for Training GANs

openai/improved-gan • • NeurIPS 2016

We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.

45
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SinGAN: Learning a Generative Model from a Single Natural Image

tamarott/SinGAN • • ICCV 2019

We introduce SinGAN, an unconditional generative model that can be learned from a single natural image.

44
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