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Deep convolutional neural networks have shown remarkable performance onvarious computer vision tasks, and yet, they are susceptible to picking upspurious correlations from the training signal. So called `shortcuts' can occurduring learning, for example, when there are specific frequencies present inthe image data that correlate with the output predictions. Both high and lowfrequencies can be characteristic of the underlying noise distribution causedby the image acquisition rather than in relation to the task-relevantinformation about the image content. Models that learn features related to thischaracteristic noise will not generalize well to new data.  In this work, we propose a simple yet effective training strategy, FrequencyDropout, to prevent convolutional neural networks from learningfrequency-specific imaging features. We employ randomized filtering of featuremaps during training which acts as a feature-level regularization. In thisstudy, we consider common image processing filters such as Gaussian smoothing,Laplacian of Gaussian, and Gabor filtering. Our training strategy ismodel-agnostic and can be used for any computer vision task. We demonstrate theeffectiveness of Frequency Dropout on a range of popular architectures andmultiple tasks including image classification, domain adaptation, and semanticsegmentation using both computer vision and medical imaging datasets. Ourresults suggest that the proposed approach does not only improve predictiveaccuracy but also improves robustness against domain shift.

Frequency Dropout: Feature-Level Regularization via Randomized Filtering