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Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.
#8 best model for
Conditional Image Generation
on CIFAR-10
CONDITIONAL IMAGE GENERATION UNSUPERVISED REPRESENTATION LEARNING
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks.
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
The adversarial loss pushes our solution to the natural image manifold using a discriminator network that is trained to differentiate between the super-resolved images and original photo-realistic images.
#10 best model for
Image Super-Resolution
on BSD100 - 4x upscaling
A Style-Based Generator Architecture for Generative Adversarial Networks
We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature.
Self-Attention Generative Adversarial Networks
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
Generative Adversarial Networks
We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake.
Coupled Generative Adversarial Networks
We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images.
#3 best model for
Image-to-Image Translation
on Cityscapes Photo-to-Labels
Boundary-Seeking Generative Adversarial Networks
We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator.
Least Squares Generative Adversarial Networks
To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator.
Semi-Supervised Learning with Generative Adversarial Networks
We extend Generative Adversarial Networks (GANs) to the semi-supervised context by forcing the discriminator network to output class labels.