  The big breakthrough on the ImageNet challenge in 2012 was partially due to
the `dropout' technique used to avoid overfitting. Here, we introduce a new
approach called `Spectral Dropout' to improve the generalization ability of
deep neural networks. We cast the proposed approach in the form of regular
Convolutional Neural Network (CNN) weight layers using a decorrelation
transform with fixed basis functions. Our spectral dropout method prevents
overfitting by eliminating weak and `noisy' Fourier domain coefficients of the
neural network activations, leading to remarkably better results than the
current regularization methods. Furthermore, the proposed is very efficient due
to the fixed basis functions used for spectral transformation. In particular,
compared to Dropout and Drop-Connect, our method significantly speeds up the
network convergence rate during the training process (roughly x2), with
considerably higher neuron pruning rates (an increase of ~ 30%). We demonstrate
that the spectral dropout can also be used in conjunction with other
regularization approaches resulting in additional performance gains.
