Computer Vision
 
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Image Generation

1996 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 implementations

Datasets

Subtasks

Most implemented papers

Most implemented Social Latest No code

Denoising Diffusion Implicit Models

ermongroup/ddim • • ICLR 2021

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.

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Instance Normalization: The Missing Ingredient for Fast Stylization

DmitryUlyanov/texture_nets • • 27 Jul 2016

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

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StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks

hanzhanggit/StackGAN • • ICCV 2017

Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications.

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Adversarial Audio Synthesis

chrisdonahue/wavegan • • ICLR 2019

Audio signals are sampled at high temporal resolutions, and learning to synthesize audio requires capturing structure across a range of timescales.

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DRAW: A Recurrent Neural Network For Image Generation

ericjang/draw • • 16 Feb 2015

This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation.

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High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

NVIDIA/pix2pixHD • • CVPR 2018

We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs).

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NICE: Non-linear Independent Components Estimation

vincentstimper/normalizing-flows • • 30 Oct 2014

It is based on the idea that a good representation is one in which the data has a distribution that is easy to model.

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AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks

taoxugit/AttnGAN • • CVPR 2018

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.

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Pixel Recurrent Neural Networks

EugenHotaj/pytorch-generative • • 25 Jan 2016

Modeling the distribution of natural images is a landmark problem in unsupervised learning.

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BEGAN: Boundary Equilibrium Generative Adversarial Networks

eriklindernoren/PyTorch-GAN • • 31 Mar 2017

We propose a new equilibrium enforcing method paired with a loss derived from the Wasserstein distance for training auto-encoder based Generative Adversarial Networks.

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