  Modern metrics for generative learning like Fr\'echet Inception Distance
(FID) demonstrate impressive performance. However, they suffer from various
shortcomings, like a bias towards specific generators and datasets. To address
this problem, we propose the Fr\'echet Wavelet Distance (FWD) as a
domain-agnostic metric based on Wavelet Packet Transform ($W_p$). FWD provides
a sight across a broad spectrum of frequencies in images with a high
resolution, along with preserving both spatial and textural aspects.
Specifically, we use Wp to project generated and dataset images to packet
coefficient space. Further, we compute Fr\'echet distance with the resultant
coefficients to evaluate the quality of a generator. This metric is
general-purpose and dataset-domain agnostic, as it does not rely on any
pre-trained network while being more interpretable because of frequency band
transparency. We conclude with an extensive evaluation of a wide variety of
generators across various datasets that the proposed FWD is able to generalize
and improve robustness to domain shift and various corruptions compared to
other metrics.
