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Image Generation

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.

State-of-the-art leaderboards

Greatest papers with code

InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

NeurIPS 2016 tensorflow/models

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.

IMAGE GENERATION REPRESENTATION LEARNING UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED MNIST

Improved Techniques for Training GANs

NeurIPS 2016 tensorflow/models

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

CONDITIONAL IMAGE GENERATION SEMI-SUPERVISED IMAGE CLASSIFICATION

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)

DENSITY ESTIMATION IMAGE GENERATION

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.

IMAGE GENERATION IMAGE STYLIZATION

Self-Attention Generative Adversarial Networks

arXiv 2018 jantic/DeOldify

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

CONDITIONAL IMAGE GENERATION

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

NeurIPS 2017 jantic/DeOldify

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

IMAGE GENERATION

A Style-Based Generator Architecture for Generative Adversarial Networks

CVPR 2019 NVlabs/stylegan

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

IMAGE GENERATION

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.

IMAGE GENERATION

Improved Training of Wasserstein GANs

NeurIPS 2017 eriklindernoren/Keras-GAN

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

CONDITIONAL IMAGE GENERATION

Wasserstein GAN

26 Jan 2017eriklindernoren/Keras-GAN

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

IMAGE GENERATION