Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks.
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
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.