  We describe a simple method for unsupervised domain adaptation, whereby the
discrepancy between the source and target distributions is reduced by swapping
the low-frequency spectrum of one with the other. We illustrate the method in
semantic segmentation, where densely annotated images are aplenty in one domain
(synthetic data), but difficult to obtain in another (real images). Current
state-of-the-art methods are complex, some requiring adversarial optimization
to render the backbone of a neural network invariant to the discrete domain
selection variable. Our method does not require any training to perform the
domain alignment, just a simple Fourier Transform and its inverse. Despite its
simplicity, it achieves state-of-the-art performance in the current benchmarks,
when integrated into a relatively standard semantic segmentation model. Our
results indicate that even simple procedures can discount nuisance variability
in the data that more sophisticated methods struggle to learn away.
