  Vanilla convolutional neural networks are known to provide superior
performance not only in image recognition tasks but also in natural language
processing and time series analysis. One of the strengths of convolutional
layers is the ability to learn features about spatial relations in the input
domain using various parameterized convolutional kernels. However, in time
series analysis learning such spatial relations is not necessarily required nor
effective. In such cases, kernels which model temporal dependencies or kernels
with broader spatial resolutions are recommended for more efficient training as
proposed by dilation kernels. However, the dilation has to be fixed a priori
which limits the flexibility of the kernels. We propose generalized dilation
networks which generalize the initial dilations in two aspects. First we derive
an end-to-end learnable architecture for dilation layers where also the
dilation rate can be learned. Second we break up the strict dilation structure,
in that we develop kernels operating independently in the input space.
