Modelling and forecasting art movements with CGANs.

ABSTRACT: Conditional generative adversarial networks (CGANs) are a recent and popular method for generating samples from a probability distribution conditioned on latent information. The latent information often comes in the form of a discrete label from a small set. We propose a novel method for training CGANs which allows us to condition on a sequence of continuous latent distributions f ⁽¹⁾, …, f ^(K). This training allows CGANs to generate samples from a sequence of distributions. We apply our method to paintings from a sequence of artistic movements, where each movement is considered to be its own distribution. Exploiting the temporal aspect of the data, a vector autoregressive (VAR) model is fitted to the means of the latent distributions that we lear

SUBMITTER: Lisi E 

PROVIDER: S-EPMC7211863 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature