Conditional Image Generation

123 papers with code • 8 benchmarks • 8 datasets

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

( Image credit: PixelCNN++ )


Use these libraries to find Conditional Image Generation models and implementations

Most implemented papers

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.

Analyzing and Improving the Image Quality of StyleGAN

NVlabs/stylegan2 CVPR 2020

Overall, our improved model redefines the state of the art in unconditional image modeling, both in terms of existing distribution quality metrics as well as perceived image quality.

Improved Training of Wasserstein GANs

igul222/improved_wgan_training NeurIPS 2017

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

Self-Attention Generative Adversarial Networks

brain-research/self-attention-gan 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.

Improved Techniques for Training GANs

openai/improved-gan 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 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.

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.

Training Generative Adversarial Networks with Limited Data

NVlabs/stylegan2-ada NeurIPS 2020

We also find that the widely used CIFAR-10 is, in fact, a limited data benchmark, and improve the record FID from 5. 59 to 2. 42.

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).

Diffusion Models Beat GANs on Image Synthesis

openai/guided-diffusion NeurIPS 2021

Finally, we find that classifier guidance combines well with upsampling diffusion models, further improving FID to 3. 94 on ImageNet 256$\times$256 and 3. 85 on ImageNet 512$\times$512.