A Design-Led Exploration of Material Interactions Between Machine Learning and Digital Portraiture

被引：0
|
作者：
Green, David Philip [1 ]
Lindley, Joseph [1 ]
Mason, Zach [1 ]
Coulton, Paul [1 ]
机构：
[1] Univ Lancaster, Imaginat Lancaster, Lancaster, England
来源：
WITH DESIGN: REINVENTING DESIGN MODES, IASDR 2021 | 2022年
基金：
英国科研创新办公室;
关键词：
Design materials; Materiality; Machine learning; Digital portraiture; Faces; Art;
D O I：
10.1007/978-981-19-4472-7_211
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
Design materials are defined as a combination of what they are, what they do, and the ways they interact with other materials. In this pictorial, we explore the interactions between machine learning (as a design material) and another design material-the human face-in the form of digital portraiture. Employing an exploratory Research through Design approach we consider how machine learning simultaneously enriches and subverts the materiality of the human face. Through a combination of images and text, we offer some considerations and provocations for further research.
引用
收藏
页码：3268 / 3283
页数：16
相关论文
共 50 条
  • [21] Design Space Exploration for Hardware Acceleration of Machine Learning Applications in MapReduce
    Neshatpour, Katayoun
    Mokrani, Hosein Mohammadi
    Sasan, Avesta
    Ghasemzadeh, Hassan
    Rafatirad, Setareh
    Homayoun, Houman
    PROCEEDINGS 26TH IEEE ANNUAL INTERNATIONAL SYMPOSIUM ON FIELD-PROGRAMMABLE CUSTOM COMPUTING MACHINES (FCCM 2018), 2018, : 221 - 221
  • [22] Integrating Machine Learning and Molecular Simulation for Material Design and Discovery
    Priyanka Sinha
    D. Roshini
    Varad Daoo
    B. Moses Abraham
    Jayant K. Singh
    Transactions of the Indian National Academy of Engineering, 2023, 8 (3) : 325 - 340
  • [23] Design Space Exploration of Emerging Memory Technologies for Machine Learning Applications
    Hasan, S. M. Shamimul
    Imam, Neena
    Kannan, Ramakrishnan
    Yoginath, Srikanth
    Kurte, Kuldeep
    2021 IEEE INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM WORKSHOPS (IPDPSW), 2021, : 439 - 448
  • [24] Efficient system design space exploration using machine learning techniques
    Ozisikyilmaz, Berkin
    Memik, Gokhan
    Choudhary, Alok
    2008 45TH ACM/IEEE DESIGN AUTOMATION CONFERENCE, VOLS 1 AND 2, 2008, : 966 - 969
  • [25] Exploration and design of Mg alloys for hydrogen storage with supervised machine learning
    Dong, Shuya
    Wang, Yingying
    Li, Jinya
    Li, Yuanyuan
    Wang, Li
    Zhang, Jinglai
    INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2023, 48 (97) : 38412 - 38424
  • [26] QMLMaterialA Quantum Machine Learning Software for Material Design and Discovery
    Lourenco, Maicon Pierre
    Herrera, Lizandra Barrios
    Hostas, Jiri
    Calaminici, Patrizia
    Koester, Andreas M.
    Tchagang, Alain
    Salahub, Dennis R.
    JOURNAL OF CHEMICAL THEORY AND COMPUTATION, 2023, 19 (17) : 5999 - 6010
  • [27] Machine Learning for Microarchitecture Power Modeling and Design Space Exploration: A Survey
    Zhai, Jianwang
    Ling, Zichao
    Bai, Chen
    Zhao, Kang
    Yu, Bei
    Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 2024, 61 (06): : 1351 - 1369
  • [28] Machine Learning Based Design Space Exploration for Hybrid Main-Memory Design
    Sen, Satyabrata
    Imam, Neena
    MEMSYS 2019: PROCEEDINGS OF THE INTERNATIONAL SYMPOSIUM ON MEMORY SYSTEMS, 2019, : 480 - 489
  • [29] Design and Development of Digital Learning Material for Applied Data Analysis
    Busstra, M. C.
    Geelen, A.
    Feskens, E. J.
    Hartog, R. J. M.
    van 't Veer, P.
    AMERICAN STATISTICIAN, 2008, 62 (04): : 329 - 339
  • [30] Machine Learning for Digital Shadow Design in Health Insurance Sector
    Rodriguez-Aguilar, Roman
    Marmolejo-Suacedo, Jose-Antonio
    Rodriguez-Aguilar, Miriam
    Marmolejo-Saucedo, Liliana
    MOBILE NETWORKS & APPLICATIONS, 2024, 29 (01): : 221 - 234
12345