Autoregressive Generative Adversarial Networks

Yasin Yazici, Kim-Hui Yap, Stefan Winkler

Abstract: Generative Adversarial Networks (GANs) learn a generative model by playing an adversarial game between a generator and an auxiliary discriminator, which classifies data samples vs. generated ones. However, it does not explicitly model feature co-occurrences in samples. In this paper, we propose a novel Autoregressive Generative Adversarial Network (ARGAN), that models the latent distribution of data using an autoregressive model, rather than relying on binary classification of samples into data/generated categories. In this way, feature co-occurrences in samples can be more efficiently captured. Our model was evaluated on two widely used datasets: CIFAR-10 and STL-10. Its performance is competitive with respect to other GAN models both quantitatively and qualitatively.

Keywords: Generative Adversarial Networks, Latent Space Modeling

Data: [CelebA](https://paperswithcode.com/dataset/celeba), [STL-10](https://paperswithcode.com/dataset/stl-10)