Wavelet Packet Power Spectrum Kullback-Leibler Divergence: A New Metric
  for Image Synthesis
Lokesh Veeramacheneni Moritz Wolter Juergen Gall
Current metrics for generative neural networks are biased towards low
frequencies, specific generators, objects from the ImageNet dataset, and value
texture more than shape. Many current quality metrics do not measure frequency
information directly. In response, we propose a new frequency band-based
quality metric, which opens a door into the frequency domain yet, at the same
time, preserves spatial aspects of the data. Our metric works well even if the
distributions we compare are far from ImageNet or have been produced by
differing generator architectures. We verify the quality of our metric by
sampling a broad selection of generative networks on a wide variety of data
sets. A user study ensures our metric aligns with human perception.
Furthermore, we show that frequency band guidance can improve the frequency
domain fidelity of a current generative network.