Conditional image generation is the task of generating new images from a dataset conditional on their class.
( Image credit: PixelCNN++ )
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We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.
#8 best model for Conditional Image Generation on CIFAR-10
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.
#9 best model for Conditional Image Generation on CIFAR-10
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks.
#7 best model for 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.
#7 best model for 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).
#2 best model for Image-to-Image Translation on ADE20K-Outdoor Labels-to-Photos
Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal.
#2 best model for Conditional Image Generation on CIFAR-10
This work explores conditional image generation with a new image density model based on the PixelCNN architecture.
#6 best model for Image Generation on CIFAR-10 (NLL Test metric)