June 11, 2024, 4:49 a.m. | Lokesh Veeramacheneni (University of Bonn), Moritz Wolter (University of Bonn), Hildegard Kuehne (University of Bonn), Juergen Gall (University of Bon

cs.LG updates on arXiv.org arxiv.org

arXiv:2312.15289v2 Announce Type: replace-cross
Abstract: 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 …

abstract arxiv bias cs.cv cs.lg datasets domain eess.iv generative generators however image image generation metrics modern performance problem replace type wavelet

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