Autoregressive Generative Adversarial NetworksDownload PDF

07 Feb 2018 (modified: 10 Feb 2022)ICLR 2018 Workshop SubmissionReaders: Everyone
Keywords: Generative Adversarial Networks, Auto-regressive Modeling
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.
Data: [CelebA](, [STL-10](
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