Abstract
The survey reviews the history and current state of computational creativity theories, focusing on generative deep learning and evaluation methods, and discusses research challenges and opportunities.
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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