Creativity and Machine Learning: A Survey

被引：5
作者：
Franceschelli, Giorgio [1 ]
Musolesi, Mirco [1 ,2 ,3 ]
机构：
[1] Univ Bologna, Alma Mater Studiorum, Bologna, Emilia Romagna, Italy
[2] UCL, London, England
[3] Univ Coll London, London, North Ireland
来源：
ACM COMPUTING SURVEYS | 2024年 / 56卷 / 11期
关键词：
Computational creativity; machine learning; generative deep learning; creativity evaluation methods; OF-THE-ART; COMPUTATIONAL CREATIVITY; GENERATION; MODEL; ALGORITHMS;
D O I：
10.1145/3664595
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
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.
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页数：41
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