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

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.