2021
DOI: 10.48550/arxiv.2104.02726
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Creativity and Machine Learning: A Survey

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, 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.1 http://www.in-vacua.com/cgi-bin/haiku.pl 2 Quite in… Show more

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Cited by 11 publications
(15 citation statements)
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“…Over the years, several computational approaches have been proposed to automatically assess the creativity in products made by (human or artificial) agents, differing in the scope of evaluation or in the method. A complete survey can be found in [13]. All of them consider value and novelty as aspects of creativity, while only some of them also consider surprise.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Over the years, several computational approaches have been proposed to automatically assess the creativity in products made by (human or artificial) agents, differing in the scope of evaluation or in the method. A complete survey can be found in [13]. All of them consider value and novelty as aspects of creativity, while only some of them also consider surprise.…”
Section: Related Workmentioning
confidence: 99%
“…The goal is to define a measure of more general applicability. Deep Learning is used for avoiding the need of identifying the required attributes to describe the artifacts or the components of creativity [13]. This leads to a measure that allows for automatic evaluation of artifacts.…”
Section: Measuring Creativity Using Deep Learningmentioning
confidence: 99%
“…Several generative models based on deep learning have been proposed in last years (for a more exhaustive list, see Franceschelli and Musolesi (2021)). However, it is possible to identify a few families of techniques: Variational Auto-Encoders (first proposed by Kingma and Welling (2014), but also used by Gregor et al (2015)); autoregressive models (first proposed by Van Den Oord et al (2016b), but also used by Van Den Oord et al (2016a,c) 2020)); sequence prediction models (very well explained by Karpathy (2015), and used by Zhang and Lapata (2014); Potash et al (2015); Sturm et al (2016); Jaques et al (2016); Lau et al (2018); Yi et al (2018); Zugarini et al (2019)); Generative Adversarial Nets (first proposed by Goodfellow et al (2014) and then used by Vernier et al (2020); modifications of the first proposal have been done by Yu et al (2017); Dong et al (2018); Elgammal et al (2017); Isola et al (2017); Karras et al (2018); Zhang et al (2019); Brock et al (2018); Karras et al (2019); Engel et al (2019)).…”
Section: Generative Deep Learningmentioning
confidence: 99%
“…There are also challenges regarding evaluation of the models. Except for generating paintings that reflect the semantics of poems and the painting style of the artist, creativity is also an important criteria and it is unclear how to evaluate the creativity yet [12].…”
Section: Zikai-poemmentioning
confidence: 99%
“…Therefore creativity should be considered as another important criteria except for pictorial quality, stylistic relevance, semantic relevance. Yet, modelling and evaluating creativity is challenging and still at its early stage [12].…”
Section: Research Challenges and Future Workmentioning
confidence: 99%