  In this paper we introduce activation functions that move the entire
computation of Convolutional Networks into the frequency domain, where they are
actually Hadamard Networks. To achieve this result we employ the properties of
Discrete Fourier Transform. We present some implementation details and
experimental results, as well as some insights into why convolutional networks
perform well in learning use cases.
