  The Generative Adversarial Network (GAN) is a state-of-the-art technique in
the field of deep learning. A number of recent papers address the theory and
applications of GANs in various fields of image processing. Fewer studies,
however, have directly evaluated GAN outputs. Those that have been conducted
focused on using classification performance, e.g., Inception Score (IS) and
statistical metrics, e.g., Fr\'echet Inception Distance (FID). Here, we
consider a fundamental way to evaluate GANs by directly analyzing the images
they generate, instead of using them as inputs to other classifiers. We
characterize the performance of a GAN as an image generator according to three
aspects: 1) Creativity: non-duplication of the real images. 2) Inheritance:
generated images should have the same style, which retains key features of the
real images. 3) Diversity: generated images are different from each other. A
GAN should not generate a few different images repeatedly. Based on the three
aspects of ideal GANs, we have designed the Likeness Score (LS) to evaluate GAN
performance, and have applied it to evaluate several typical GANs. We compared
our proposed measure with two commonly used GAN evaluation methods: IS and FID,
and four additional measures. Furthermore, we discuss how these evaluations
could help us deepen our understanding of GANs and improve their performance.
