Creativity and Machine Learning: A Survey-Reference-Cited by-同舟云学术

Creativity and Machine Learning: A Survey

Published:2024-06-28 Issue:11 Volume:56 Page:1-41
ISSN:0360-0300
Container-title:ACM Computing Surveys
language:en
Short-container-title:ACM Comput. Surv.

Author:

Franceschelli Giorgio¹^ORCID,Musolesi Mirco²^ORCID

Affiliation:

1. Alma Mater Studiorum Università di Bologna, Bologna, Italy

2. University College London, London, United Kingdom of Great Britain and Northern Ireland and Alma Mater Studiorum Università di Bologna, Bologna, Italy

Abstract

There is a growing interest in the area of machine learning and creativity. This survey presents an overview of the history and the state of the art of computational creativity theories, key machine learning techniques (including generative deep learning), and corresponding automatic evaluation methods. After presenting a critical discussion of the key contributions in this area, we outline the current research challenges and emerging opportunities in this field.

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/3664595

Reference312 articles.

1. ArtEmis: Affective Language for Visual Art

2. The emotionally intelligent use of attention and affective arousal under creative frustration and creative success

3. Andrea Agostinelli Timo I. Denk Zalán Borsos Jesse Engel Mauro Verzetti Antoine Caillon Qingqing Huang Aren Jansen Adam Roberts Marco Tagliasacchi Matt Sharifi Neil Zeghidour and Christian Frank. 2023. MusicLM: Generating Music From Text. (2023). arXiv:2301.11325

4. Hassan Akbari, Liangzhe Yuan, Rui Qian, Wei-Hong Chuang, Shih-Fu Chang, Yin Cui, and Boqing Gong. 2021. VATT: Transformers for multimodal self-supervised learning from raw video, audio and text. In Advances in Neural Information Processing Systems (NeurIPS’21).

5. Andrei G. Aleinikov, Sharon Kackmeister, and Ron Koenig. 2000. Creating Creativity: 101 Definitions (what Webster Never Told You). Alden B. Dow Creativity Center Press, Midland, MI.

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