Browse > Computer Vision > Image Generation

# Image Generation Edit

214 papers with code · Computer Vision

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.

Trend Dataset Best Method Paper title Paper Code Compare

# InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

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.

58,495

# 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.

58,495

# Density estimation using Real NVP

27 May 2016tensorflow/models

Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning.

#14 best model for Image Generation on CIFAR-10 (NLL Test metric)

58,495

# Instance Normalization: The Missing Ingredient for Fast Stylization

27 Jul 2016lengstrom/fast-style-transfer

It this paper we revisit the fast stylization method introduced in Ulyanov et.

8,412

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

8,186

# 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.

8,186

# A Style-Based Generator Architecture for Generative Adversarial Networks

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

8,019

# Generating Diverse High-Fidelity Images with VQ-VAE-2

2 Jun 2019deepmind/sonnet

We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation.

7,968

# Improved Training of Wasserstein GANs

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

5,810

# Wasserstein GAN

26 Jan 2017eriklindernoren/Keras-GAN

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

5,810