🤖 AI Summary
提出基于小波包变换的Fréchet Wavelet Distance(FWD)指标，解决现有生成图像评价指标对特定生成器和数据集的偏见问题，通过计算小波包系数空间的Fréchet距离，实现领域无关且更可解释的质量评估。
📝 Abstract
Modern metrics for generative learning like Fr'echet Inception Distance (FID) and DINOv2-Fr'echet 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 Fr'echet Wavelet Distance (FWD) as a domain-agnostic metric based on the Wavelet Packet Transform ($W_p$). FWD provides a sight across a broad spectrum of frequencies in images with a high resolution, preserving both spatial and textural aspects. Specifically, we use $W_p$ to project generated and real images to the packet coefficient space. We then compute the 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 due to its ability to compute Fr'echet distance per packet, enhancing transparency. We conclude with an extensive evaluation of a wide variety of generators across various datasets that the proposed FWD can generalize and improve robustness to domain shifts and various corruptions compared to other metrics.
