  We devise a universal adaptive neural layer to "learn" optimal frequency
filter for each image together with the weights of the base neural network that
performs some computer vision task. The proposed approach takes the source
image in the spatial domain, automatically selects the best frequencies from
the frequency domain, and transmits the inverse-transform image to the main
neural network. Remarkably, such a simple add-on layer dramatically improves
the performance of the main network regardless of its design. We observe that
the light networks gain a noticeable boost in the performance metrics; whereas,
the training of the heavy ones converges faster when our adaptive layer is
allowed to "learn" alongside the main architecture. We validate the idea in
four classical computer vision tasks: classification, segmentation, denoising,
and erasing, considering popular natural and medical data benchmarks.
