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
Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks.
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
We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature.
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 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.
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.
We extend Generative Adversarial Networks (GANs) to the semi-supervised context by forcing the discriminator network to output class labels.