Image Generation
2036 papers with code • 85 benchmarks • 67 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
- Conditional Image Generation
- Conditional Image Generation
- Face Generation
- 3D Generation
- Image Harmonization
- Pose Transfer
- 3D-Aware Image Synthesis
- Facial Inpainting
- Layout-to-Image Generation
- 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
Most implemented papers
Denoising Diffusion Implicit Models
Denoising diffusion probabilistic models (DDPMs) have achieved high quality image generation without adversarial training, yet they require simulating a Markov chain for many steps to produce a sample.
Instance Normalization: The Missing Ingredient for Fast Stylization
It this paper we revisit the fast stylization method introduced in Ulyanov et.
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications.
Adversarial Audio Synthesis
Audio signals are sampled at high temporal resolutions, and learning to synthesize audio requires capturing structure across a range of timescales.
DRAW: A Recurrent Neural Network For Image Generation
This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation.
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs).
NICE: Non-linear Independent Components Estimation
It is based on the idea that a good representation is one in which the data has a distribution that is easy to model.
AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks
In this paper, we propose an Attentional Generative Adversarial Network (AttnGAN) that allows attention-driven, multi-stage refinement for fine-grained text-to-image generation.
Pixel Recurrent Neural Networks
Modeling the distribution of natural images is a landmark problem in unsupervised learning.
BEGAN: Boundary Equilibrium Generative Adversarial Networks
We propose a new equilibrium enforcing method paired with a loss derived from the Wasserstein distance for training auto-encoder based Generative Adversarial Networks.