  Deep generative models, such as Variational Autoencoders (VAEs), have been
employed widely in computational creativity research. However, such models
discourage out-of-distribution generation to avoid spurious sample generation,
limiting their creativity. Thus, incorporating research on human creativity
into generative deep learning techniques presents an opportunity to make their
outputs more compelling and human-like. As we see the emergence of generative
models directed to creativity research, a need for machine learning-based
surrogate metrics to characterize creative output from these models is
imperative. We propose group-based subset scanning to quantify, detect, and
characterize creative processes by detecting a subset of anomalous
node-activations in the hidden layers of generative models. Our experiments on
original, typically decoded, and "creatively decoded" (Das et al 2020) image
datasets reveal that the proposed subset scores distribution is more useful for
detecting creative processes in the activation space rather than the pixel
space. Further, we found that creative samples generate larger subsets of
anomalies than normal or non-creative samples across datasets. The node
activations highlighted during the creative decoding process are different from
those responsible for normal sample generation.
