Quality and diversity have been proposed as reasonable heuristics for
assessing content generated by co-creative systems, but to date there has been
little agreement around what constitutes the latter or how to measure it.
Proposed approaches for assessing generative models in terms of diversity have
limitations in that they compare the model's outputs to a ground truth that in
the era of large pre-trained generative models might not be available, or
entail an impractical number of computations. We propose an alternative based
on entropy of neural network encodings for comparing diversity between sets of
images that does not require ground-truth knowledge and is easy to compute. We
also compare two pre-trained networks and show how the choice relates to the
notion of diversity that we want to evaluate. We conclude with a discussion of
the potential applications of these measures for ideation in interactive
systems, model evaluation, and more broadly within computational creativity.

通过将神经网络编码的熵用于比较图像集合之间的多样性，我们提出了一种无需基准知识且易于计算的方法来评估协同创作系统生成的内容的多样性。通过比较两个预训练网络的选择与我们想要评估的多样性概念的关系，我们还讨论了这些度量在交互系统中的概念生成、模型评估以及计算创造力的更广泛应用。