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.
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