  Deep neural networks often consist of a great number of trainable parameters
for extracting powerful features from given datasets. On one hand, massive
trainable parameters significantly enhance the performance of these deep
networks. On the other hand, they bring the problem of over-fitting. To this
end, dropout based methods disable some elements in the output feature maps
during the training phase for reducing the co-adaptation of neurons. Although
the generalization ability of the resulting models can be enhanced by these
approaches, the conventional binary dropout is not the optimal solution.
Therefore, we investigate the empirical Rademacher complexity related to
intermediate layers of deep neural networks and propose a feature distortion
method (Disout) for addressing the aforementioned problem. In the training
period, randomly selected elements in the feature maps will be replaced with
specific values by exploiting the generalization error bound. The superiority
of the proposed feature map distortion for producing deep neural network with
higher testing performance is analyzed and demonstrated on several benchmark
image datasets.
