  Deep convolutional neural networks have shown remarkable performance on
various computer vision tasks, and yet, they are susceptible to picking up
spurious correlations from the training signal. So called `shortcuts' can occur
during learning, for example, when there are specific frequencies present in
the image data that correlate with the output predictions. Both high and low
frequencies can be characteristic of the underlying noise distribution caused
by the image acquisition rather than in relation to the task-relevant
information about the image content. Models that learn features related to this
characteristic noise will not generalize well to new data.
  In this work, we propose a simple yet effective training strategy, Frequency
Dropout, to prevent convolutional neural networks from learning
frequency-specific imaging features. We employ randomized filtering of feature
maps during training which acts as a feature-level regularization. In this
study, we consider common image processing filters such as Gaussian smoothing,
Laplacian of Gaussian, and Gabor filtering. Our training strategy is
model-agnostic and can be used for any computer vision task. We demonstrate the
effectiveness of Frequency Dropout on a range of popular architectures and
multiple tasks including image classification, domain adaptation, and semantic
segmentation using both computer vision and medical imaging datasets. Our
results suggest that the proposed approach does not only improve predictive
accuracy but also improves robustness against domain shift.
