  While the potential of deep learning (DL) for automating simple tasks is
already well explored, recent research has started investigating the use of
deep learning for creative design, both for complete artifact creation and
supporting humans in the creation process. In this paper, we use insights from
computational creativity to conceptualize and assess current applications of
generative deep learning in creative domains identified in a literature review.
We highlight parallels between current systems and different models of human
creativity as well as their shortcomings. While deep learning yields results of
high value, such as high-quality images, their novelty is typically limited due
to multiple reasons such as being tied to a conceptual space defined by
training data. Current DL methods also do not allow for changes in the internal
problem representation, and they lack the capability to identify connections
across highly different domains, both of which are seen as major drivers of
human creativity.
