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

共 50 条

[41] Tessellating the Latent Space for Non-Adversarial Generative Auto-Encoders
Gai, Kuo
Zhang, Shihua
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2024, 46 (02) : 780 - 792
[42] On Disentanglement and Mutual Information in Semi-Supervised Variational Auto-Encoders
Gordon Rodriguez, Elliott
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW 2021, 2021, : 1257 - 1262
[43] Unsupervised Phonocardiogram Analysis With Distribution Density Based Variational Auto-Encoders
Li, Shengchen
Tian, Ke
FRONTIERS IN MEDICINE, 2021, 8
[44] VARIATIONAL AUTO-ENCODERS WITHOUT GRAPH COARSENING FOR FINE MESH LEARNING
Vercheval, Nicolas
De Bie, Hendrik
Pizurica, Aleksandra
2020 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2020, : 2681 - 2685
[45] Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders
Zahirnia, Kiarash
Schulte, Oliver
Naddaf, Parmis
Li, Ke
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 35 (NEURIPS 2022), 2022,
[46] Towards Deeper Understanding of Variational Auto-encoders for Binary Collaborative Filtering
Zamani, Siamak
Li, Dingcheng
Fei, Hongliang
Li, Ping
PROCEEDINGS OF THE 2022 ACM SIGIR INTERNATIONAL CONFERENCE ON THE THEORY OF INFORMATION RETRIEVAL, ICTIR 2022, 2022, : 175 - 184
[47] Dynamic Feature Collaborative Variational Auto-Encoders for Academic Paper Recommendation
Niu, Yuanhao
Jiang, Ting
Chen, Zhiheng
Bai, Weichen
PROCEEDINGS OF 2023 7TH INTERNATIONAL CONFERENCE ON ELECTRONIC INFORMATION TECHNOLOGY AND COMPUTER ENGINEERING, EITCE 2023, 2023, : 1620 - 1627
[48] Attribute-based regularization of latent spaces for variational auto-encoders
Pati, Ashis
Lerch, Alexander
NEURAL COMPUTING & APPLICATIONS, 2021, 33 (09): : 4429 - 4444
[49] Interpretable ECG Beat Embedding using Disentangled Variational Auto-Encoders
Van Steenkiste, Tom
Deschrijver, Dirk
Dhaene, Tom
2019 IEEE 32ND INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS), 2019, : 373 - 378
[50] FMCW Radar Sensing for Indoor Drones Using Variational Auto-Encoders
Safa, Ali
Verbelen, Tim
Catal, Ozan
Van de Maele, Toon
Hartmann, Matthias
Dhoedt, Bart
Bourdoux, Andre
2023 IEEE RADAR CONFERENCE, RADARCONF23, 2023,

← 1 2 3 4 5 →