Abstract:
A fundamental processing layer of modern deep neural networks is the 2D convolution. It applies a filter uniformly across the input, effectively creating feature detectors that are translation invariant. In contrast, fully-connected layers are spatially selective, allowing unique detectors across the input. However, full connectivity comes at the expense of an enormous number of free parameters to be trained, the associated difficulty in learning without over-fitting, and the loss of spatial coherence. We introduce Contextual Convolution Blocks, a novel method to create spatially selective feature detectors that are locally translation invariant. This increases the expressive power of the network beyond standard convolutional layers and allows learning unique filters for distinct regions of the input. The filters no longer need to be discriminative in regions not likely to contain the target features. This is a generalization of the Squeeze-and-Excitation architecture that introduces minimal extra parameters. We provide experimental results on three datasets and a thorough exploration into how the increased expressiveness is instantiated.
