Machine Learning in Evolving Art Styles: A Study of Algorithmic Creativity
Machine learning (ML) has played an increasingly pivotal role in shaping and evolving artistic expression, leading to new forms of algorithmic creativity. In this study, we explore how ML models, particularly deep learning algorithms such as generative adversarial networks (GANs), have contributed t...
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MDPI AG
2025-04-01
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| Series: | Engineering Proceedings |
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| Online Access: | https://www.mdpi.com/2673-4591/92/1/45 |
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| author | Wai Yie Leong |
| author_facet | Wai Yie Leong |
| author_sort | Wai Yie Leong |
| collection | DOAJ |
| description | Machine learning (ML) has played an increasingly pivotal role in shaping and evolving artistic expression, leading to new forms of algorithmic creativity. In this study, we explore how ML models, particularly deep learning algorithms such as generative adversarial networks (GANs), have contributed to evolving art styles by learning from vast datasets of historical and contemporary artworks. These algorithms mimic artistic techniques, generate new styles, and even create novel art forms that blend or deviate from traditional artistic boundaries. The challenges of algorithmic creativity, such as concerns about authorship, originality, and the potential loss of human touch in art are also highlighted. The role of machine learning in art raises important philosophical and ethical questions about the nature of creativity and the evolving relationship between human artists and machines. Machine learning has become a powerful tool in expanding the possibilities of artistic expression. While AI-generated art challenges traditional notions of creativity, it also opens up new horizons for collaboration and innovation in art, potentially leading to entirely new art styles in the digital age. |
| format | Article |
| id | doaj-art-7cc0dadef876499faaa4f09b413256a7 |
| institution | Kabale University |
| issn | 2673-4591 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj-art-7cc0dadef876499faaa4f09b413256a72025-08-20T03:26:52ZengMDPI AGEngineering Proceedings2673-45912025-04-019214510.3390/engproc2025092045Machine Learning in Evolving Art Styles: A Study of Algorithmic CreativityWai Yie Leong0Faculty of Engineering and Quantity Surveying, INTI International University, Persiaran Perdana BBN, Putra Nilai, Nilai 71800, MalaysiaMachine learning (ML) has played an increasingly pivotal role in shaping and evolving artistic expression, leading to new forms of algorithmic creativity. In this study, we explore how ML models, particularly deep learning algorithms such as generative adversarial networks (GANs), have contributed to evolving art styles by learning from vast datasets of historical and contemporary artworks. These algorithms mimic artistic techniques, generate new styles, and even create novel art forms that blend or deviate from traditional artistic boundaries. The challenges of algorithmic creativity, such as concerns about authorship, originality, and the potential loss of human touch in art are also highlighted. The role of machine learning in art raises important philosophical and ethical questions about the nature of creativity and the evolving relationship between human artists and machines. Machine learning has become a powerful tool in expanding the possibilities of artistic expression. While AI-generated art challenges traditional notions of creativity, it also opens up new horizons for collaboration and innovation in art, potentially leading to entirely new art styles in the digital age.https://www.mdpi.com/2673-4591/92/1/45machine learningdeep learning algorithmcontemporary artworksprocess innovation |
| spellingShingle | Wai Yie Leong Machine Learning in Evolving Art Styles: A Study of Algorithmic Creativity Engineering Proceedings machine learning deep learning algorithm contemporary artworks process innovation |
| title | Machine Learning in Evolving Art Styles: A Study of Algorithmic Creativity |
| title_full | Machine Learning in Evolving Art Styles: A Study of Algorithmic Creativity |
| title_fullStr | Machine Learning in Evolving Art Styles: A Study of Algorithmic Creativity |
| title_full_unstemmed | Machine Learning in Evolving Art Styles: A Study of Algorithmic Creativity |
| title_short | Machine Learning in Evolving Art Styles: A Study of Algorithmic Creativity |
| title_sort | machine learning in evolving art styles a study of algorithmic creativity |
| topic | machine learning deep learning algorithm contemporary artworks process innovation |
| url | https://www.mdpi.com/2673-4591/92/1/45 |
| work_keys_str_mv | AT waiyieleong machinelearninginevolvingartstylesastudyofalgorithmiccreativity |