Conditional image generation is the task of generating new images from a dataset conditional on their class.
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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.
#5 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.
#6 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.
#4 best model for Conditional Image Generation on ImageNet 128x128
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
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.
#4 best model for Conditional Image Generation on CIFAR-10
We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the underlining probabilistic model.
#5 best model for Conditional Image Generation on ImageNet 128x128
Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions.
#4 best model for Image-to-Image Translation on RaFD