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++ )
We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.
Ranked #14 on Conditional Image Generation on CIFAR-10
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.
Ranked #8 on Image Clustering on Tiny-ImageNet
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks.
Ranked #12 on Conditional Image Generation on ImageNet 128x128
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.
Ranked #13 on Conditional Image Generation on CIFAR-10
We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs).
Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal.
Ranked #2 on Image Generation on ImageNet 128x128
On this foundation, we propose the Rebooted Auxiliary Classifier Generative Adversarial Network (ReACGAN).
Ranked #1 on Conditional Image Generation on CIFAR-10
The discriminator of ContraGAN discriminates the authenticity of given samples and minimizes a contrastive objective to learn the relations between training images.
Ranked #9 on Conditional Image Generation on CIFAR-10 (FID metric)