Image Generation
2568 papers with code • 86 benchmarks • 73 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 implementationsDatasets
Subtasks
- Image-to-Image Translation
- Text-to-Image Generation
- Image Inpainting
- Image Manipulation
- Image Manipulation
- Conditional Image Generation
- Face Generation
- 3D Generation
- Image Harmonization
- Pose Transfer
- Facial Inpainting
- Layout-to-Image Generation
- 3D-Aware Image Synthesis
- ROI-based image generation
- Image Generation from Scene Graphs
- Pose-Guided Image Generation
- User Constrained Thumbnail Generation
- Handwritten Word Generation
- Chinese Landscape Painting Generation
- person reposing
- Infinite Image Generation
- Multi class one-shot image synthesis
- Single class few-shot image synthesis
- Text within image generation
Most implemented papers
Analyzing and Improving the Image Quality of StyleGAN
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.
Wasserstein GAN
We introduce a new algorithm named WGAN, an alternative to traditional GAN training.
Progressive Growing of GANs for Improved Quality, Stability, and Variation
We describe a new training methodology for generative adversarial networks.
Improved Training of Wasserstein GANs
Generative Adversarial Networks (GANs) are powerful generative models, but suffer from training instability.
A Style-Based Generator Architecture for Generative Adversarial Networks
We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature.
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Generative Adversarial Networks (GANs) excel at creating realistic images with complex models for which maximum likelihood is infeasible.
Denoising Diffusion Probabilistic Models
We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics.
Self-Attention Generative Adversarial Networks
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks.
Improved Techniques for Training GANs
We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.
SinGAN: Learning a Generative Model from a Single Natural Image
We introduce SinGAN, an unconditional generative model that can be learned from a single natural image.