Learning 3D-FilterMap for Deep Convolutional Neural Networks
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We present a novel and compact architecture for deep Convolutional Neural Networks (CNNs) in this paper, termed 3D-FilterMap Convolutional Neural Networks (3D-FM-CNNs). The convolution layer of 3D-FM-CNN learns a compact representation of the filters, named 3D-FilterMap, instead of a set of independent filters in the conventional convolution layer. The filters are extracted from the 3D-FilterMap as overlapping 3D submatrics with weight sharing among nearby filters, and these filters are convolved with the input to generate the output of the convolution layer for 3D-FM-CNN. Due to the weight sharing scheme, the parameter size of the 3D-FilterMap is much smaller than that of the filters to be learned in the conventional convolution layer when 3D-FilterMap generates the same number of filters. Our work is fundamentally different from the network compression literature that reduces the size of a learned large network in the sense that a small network is directly learned from scratch. Experimental results demonstrate that 3D-FM-CNN enjoys a small parameter space by learning compact 3D-FilterMaps, while achieving performance compared to that of the baseline CNNs which learn the same number of filters as that generated by the corresponding 3D-FilterMap.
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