부산대학교 도서관

In this paper, we present Wasserstein Inception Distance (WInD), a novel metric for evaluating performance of Generative Adversarial Networks (GANs). The proposed metric extends on the rationale of the previously proposed Frechet Inception Distance (FID), in the sense that GAN performance is quantified in terms of data and model distribution divergence. We extend FID by relaxing the Gaussian hypothesis of the related inception features and extend it for non-Gaussian, multimodal distributions. Gaussian Mixture Models (GMMs) are used to model data and model inception features, and the Wasserstein distance is employed as a pdf matching metric. We show that the proposed WInD metric inherits the desirable features of FID and correlates well with actual GAN performance. Furthermore, WInD can correctly evaluate cases were data and model distribution erroneously would appear as well peforming using FID. Numerical experiments on synthetic and real datasets validate our claim.