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.
Ranked #9 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 #3 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 #9 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 #8 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).
Ranked #3 on Image-to-Image Translation on Cityscapes Labels-to-Photo (Per-pixel Accuracy metric)
Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal.
Ranked #1 on Image Generation on ImageNet 128x128
This work explores conditional image generation with a new image density model based on the PixelCNN architecture.
Ranked #3 on Image Generation on ImageNet 32x32