Modern metrics for generative learning like Fréchet Inception Distance (FID) and DINOv2-Fréchet Distance (FD-DINOv2) demonstrate impressive performance. However, they suffer from various shortcomings, like a bias towards specific generators and datasets. To address this problem, we propose the Fréchet Wavelet Distance (FWD) as a domain-agnostic metric based on the Wavelet Packet Transform (
We propose the Fréchet Wavelet Distace (FWD) to primarily tackle the dataset-domain bias which inturn leverages Wavelet Packet Transform (
The metric is computed in three steps. First, we compute the
Since FWD averages Fréchet distances across all frequency bands, this design choice allows one to understand overall score. The below figure depicts per-packet FWD for both StyleGAN2 and DDGAN. We observe frequency characteristics of DDGAN images are near to original dataset compared to StyleGAN2 images.
pip install pytorchfwd
@inproceedings{
veeramacheneni2025fwd,
title={Fr\'echet Wavelet Distance: A Domain-Agnostic Metric for Image Generation},
author={Lokesh Veeramacheneni and Moritz Wolter and Hildegard Kuehne and Juergen Gall},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=QinkNNKZ3b}
}