Conditional Image Generation

83 papers with code • 7 benchmarks • 4 datasets

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

( Image credit: PixelCNN++ )

Greatest papers with code

Improved Techniques for Training GANs

tensorflow/models NeurIPS 2016

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

tensorflow/models 19 Nov 2015

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

Conditional Image Generation Image Clustering +1

Making Convolutional Networks Shift-Invariant Again

rwightman/pytorch-image-models 25 Apr 2019

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

Classification Consistency Conditional Image Generation

Self-Attention Generative Adversarial Networks

jantic/DeOldify arXiv 2018

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

Improved Training of Wasserstein GANs

eriklindernoren/PyTorch-GAN NeurIPS 2017

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

eriklindernoren/PyTorch-GAN ICML 2017

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

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).

Conditional Image Generation Fundus to Angiography Generation +2

Large Scale GAN Training for High Fidelity Natural Image Synthesis

ajbrock/BigGAN-PyTorch ICLR 2019

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

ContraGAN: Contrastive Learning for Conditional Image Generation

POSTECH-CVLab/PyTorch-StudioGAN NeurIPS 2020

The discriminator of ContraGAN discriminates the authenticity of given samples and minimizes a contrastive objective to learn the relations between training images.

Conditional Image Generation Contrastive Learning +1