About

Conditional image generation is the task of generating new images from a dataset conditional on their class.

( Image credit: PixelCNN++ )

Benchmarks

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Datasets

Greatest papers with code

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

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

19 Nov 2015tensorflow/models

In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.

CONDITIONAL IMAGE GENERATION IMAGE CLUSTERING UNSUPERVISED REPRESENTATION LEARNING

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

Making Convolutional Networks Shift-Invariant Again

25 Apr 2019rwightman/pytorch-image-models

The well-known signal processing fix is anti-aliasing by low-pass filtering before downsampling.

CLASSIFICATION CONSISTENCY CONDITIONAL IMAGE GENERATION

Improved Training of Wasserstein GANs

NeurIPS 2017 eriklindernoren/PyTorch-GAN

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

CONDITIONAL IMAGE GENERATION SYNTHETIC DATA GENERATION

Conditional Image Synthesis With Auxiliary Classifier GANs

ICML 2017 eriklindernoren/PyTorch-GAN

We expand on previous work for image quality assessment to provide two new analyses for assessing the discriminability and diversity of samples from class-conditional image synthesis models.

CONDITIONAL IMAGE GENERATION IMAGE QUALITY ASSESSMENT

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

CVPR 2018 NVIDIA/pix2pixHD

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

CONDITIONAL IMAGE GENERATION FUNDUS TO ANGIOGRAPHY GENERATION INSTANCE SEGMENTATION SEMANTIC SEGMENTATION

Large Scale GAN Training for High Fidelity Natural Image Synthesis

ICLR 2019 ajbrock/BigGAN-PyTorch

Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal.

CONDITIONAL IMAGE GENERATION

Conditional Image Generation with PixelCNN Decoders

NeurIPS 2016 openai/pixel-cnn

This work explores conditional image generation with a new image density model based on the PixelCNN architecture.

CONDITIONAL IMAGE GENERATION

High-Fidelity Image Generation With Fewer Labels

6 Mar 2019google/compare_gan

Deep generative models are becoming a cornerstone of modern machine learning.

CONDITIONAL IMAGE GENERATION