Generation and Extraction of Color Palettes with Adversarial Variational Auto-Encoders

The process of creating a meaningful and perceptually pleasing color palette is an incredibly difficult task for the inexperienced practitioner. In this paper we show that the Variational Auto Encoder can be a powerful creative tool for the generation of novel color palettes as well as their extraction from visual mediums. Our proposed model is capable of extracting meaningful color palettes from images, and simultaneously learns an internal representation which allows for the sampling of novel color palettes without any additional input.