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Current metrics for generative neural networks are biased towards lowfrequencies, specific generators, objects from the ImageNet dataset, and valuetexture more than shape. Many current quality metrics do not measure frequencyinformation directly. In response, we propose a new frequency band-basedquality metric, which opens a door into the frequency domain yet, at the sametime, preserves spatial aspects of the data. Our metric works well even if thedistributions we compare are far from ImageNet or have been produced bydiffering generator architectures. We verify the quality of our metric bysampling a broad selection of generative networks on a wide variety of datasets. A user study ensures our metric aligns with human perception.Furthermore, we show that frequency band guidance can improve the frequencydomain fidelity of a current generative network.

Wavelet Packet Power Spectrum Kullback-Leibler Divergence: A New Metric for Image Synthesis