Revisiting the Evaluation of Image Synthesis with GANs

A good metric, which promises a reliable comparison between solutions, is essential to a well-defined task. Unlike most vision tasks that have per-sample ground-truth, image synthesis targets generating \emph{unseen} data and hence is usually evaluated with a distributional distance between one set of real samples and another set of generated samples. This work provides an empirical study on the evaluation of synthesis performance by taking the popular generative adversarial networks (GANs) as a representative of generative models. In particular, we make in-depth analyses on how to represent a data point in the feature space, how to calculate a fair distance using selected samples, and how many instances to use from each set. Experiments on multiple datasets and settings suggest that (1) a group of models including both CNN-based and ViT-based architectures serve as reliable and robust feature extractors, (2) Centered Kernel Alignment (CKA) enables better comparison across various extractors and hierarchical layers in one model, and (3) CKA shows satisfactory sample efficiency and complements existing metrics (\textit{e.g.}, FID) in characterizing the similarity between two internal data correlations. These findings help us design a new measurement system, based on which we re-evaluate the state-of-the-art generative models in a consistent and reliable way.