  We propose a generalized convolutional neural network (CNN) architecture that
first decomposes the input signal into subbands by an adaptive filter bank
structure, and then uses convolutional layers to extract features from each
subband independently. Fully connected layers finally combine the extracted
features to perform classification. The proposed architecture restrains each of
the subband CNNs from learning using the entire input signal spectrum,
resulting in structural regularization. Our proposed CNN architecture is fully
compatible with the end-to-end learning mechanism of typical CNN architectures
and learns the subband decomposition from the input dataset. We show that the
proposed CNN architecture has attractive properties, such as robustness to
input and weight-and-bias quantization noise, compared to regular full-band CNN
architectures. Importantly, the proposed architecture significantly reduces
computational costs, while maintaining state-of-the-art classification
accuracy.
  Experiments on image classification tasks using the MNIST, CIFAR-10/100,
Caltech-101, and ImageNet-2012 datasets show that the proposed architecture
allows accuracy surpassing state-of-the-art results. On the ImageNet-2012
dataset, we achieved top-5 and top-1 validation set accuracy of 86.91% and
69.73%, respectively. Notably, the proposed architecture offers over 90%
reduction in computation cost in the inference path and approximately 75%
reduction in back-propagation (per iteration) with just a single-layer subband
decomposition. With a 2-layer subband decomposition, the computational gains
are even more significant with comparable accuracy results to the single-layer
decomposition.
