  Convolutional neural networks are based on a huge number of trained weights.
Consequently, they are often data-greedy, sensitive to overtraining, and learn
slowly. We follow the line of research in which filters of convolutional neural
layers are determined on the basis of a smaller number of trained parameters.
In this paper, the trained parameters define a frequency spectrum which is
transformed into convolutional filters with Inverse Discrete Cosine Transform
(IDCT, the same is applied in decompression from JPEG). We analyze how
switching off selected components of the spectra, thereby reducing the number
of trained weights of the network, affects its performance. Our experiments
show that coding the filters with trained DCT parameters leads to improvement
over traditional convolution. Also, the performance of the networks modified
this way decreases very slowly with the increasing extent of switching off
these parameters. In some experiments, a good performance is observed when even
99.9% of these parameters are switched off.
