Coupled convolution layer for convolutional neural network.

Abstract

We propose a coupled convolution layer comprising multiple parallel convolutions with mutually constrained filters. Inspired by biological human vision mechanism, we constrain the convolution filters such that one set of filter weights should be geometrically rotated, mirrored, or be the negative of the other. Our analysis suggests that the coupled convolution layer is more effective for lower layer where feature maps preserve geometric properties. Experimental comparisons demonstrate that the proposed coupled convolution layer performs slightly better than the original layer while decreasing the number of parameters. We evaluate its effect compared to non-constrained convolution layer using the CIFAR-10, CIFAR-100, and PlanktonSet 1.0 datasets.