Creativity and Machine Learning: A Survey

2021-04-06

ACM Computing Surveys

AI Summary

This paper addresses the conceptual and methodological challenges in bridging machine learning and computational creativity. It systematically traces the evolution of computational creativity theory and surveys key generative deep learning techniques—namely, Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformers—alongside their applications in creative tasks. To overcome persistent evaluation bottlenecks, the authors propose a hybrid assessment framework integrating cognitive modeling, multi-dimensional aesthetic metrics, and human-grounded benchmarks—marking the first comprehensive integration of theoretical paradigms with generative model practice. The core contributions are threefold: (1) construction of the most comprehensive research map of computational creativity to date; (2) a cross-paradigmatic methodological reflection on evaluation; and (3) identification of explainability enhancement and human-AI co-creation as critical frontiers—thereby establishing theoretical consensus and practical guidelines for algorithm design, evaluation standardization, and interdisciplinary deployment.

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.

Problem

• Survey machine learning techniques
• Explore computational creativity theories
• Evaluate automatic creativity methods

Innovation

• Generative deep learning techniques
• Automatic evaluation methods
• Computational creativity theories