Connections Between Pairs of Filters Improve the Accuracy of Convolutional Neural Networks

2026-06-11

AI Summary

This work proposes a learnable inter-filter connectivity mechanism that replaces the fixed pointwise nonlinear activations in conventional convolutional neural networks with a parameterized, universal connection function embedded within convolutional layers. By enabling adaptive interactions among filters, the approach overcomes the limitations of traditional fixed logical operations—such as multiplication or minimum selection—and allows the network to automatically optimize its connectivity strategy through end-to-end training. Experimental results demonstrate that this method significantly improves classification accuracy, confirming its effectiveness in enhancing both model expressivity and generalization capability.

Abstract

While researchers continue to find new and improved network structures for CNNs, most of the newly invented architectures still rely on the traditional pattern of stacking convolutional blocks and separating them with pointwise activation functions. However, there are drawbacks to a network purely building on pointwise nonlinearities. One alternative is to introduce a pairwise connection between two filters of a network. Typical connection functions use multiplications or the minimum operation to realize logical AND connections. In this paper, we go one step further by demonstrating that CNNs can benefit from more general connections, which include parameters that are learned. With such parameters, the network is able to implement different connections in different network layers and better adapt the connection function to the task at hand.

Problem

• convolutional neural networks
• filter connections
• learnable connections
• nonlinearities
• network architecture

Innovation

• pairwise filter connections
• learnable connection functions
• convolutional neural networks
• adaptive nonlinearity
• parameterized interactions