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

被引：1

作者：

Moussa, Ahmad ^{[1
]}

Watanabe, Hiroshi ^{[1
]}

机构：

[1] Waseda Univ, Grad Sch Fundamental Sci & Engn, Tokyo, Japan

来源：

PROCEEDINGS OF SIXTH INTERNATIONAL CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGY (ICICT 2021), VOL 2 | 2022年 / 236卷

关键词：

Variational auto-encoder; Color palettes; Generative adversarial networks;

D O I：

10.1007/978-981-16-2380-6_78

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：889 / 897

页数：9

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