Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Informing Artificial Intelligence Generative Techniques using Cognitive
  Theories of Human Creativity   The common view that our creativity is what makes us uniquely human suggests
that incorporating research on human creativity into generative deep learning
techniques might be a fruitful avenue for making their outputs more compelling
and human-like. Using an original synthesis of Deep Dream-based convolutional
neural networks and cognitive based computational art rendering systems, we
show how honing theory, intrinsic motivation, and the notion of a 'seed
incident' can be implemented computationally, and demonstrate their impact on
the resulting generative art. Conversely, we discuss how explorations in deep
learn-ing convolutional neural net generative systems can inform our
understanding of human creativity. We conclude with ideas for further
cross-fertilization between AI based computational creativity and psychology of
creativity.

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The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
32
The categories of this paper are: atom-ph
14
The categories of this paper are: math-ph
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an