Algebraic Representations for Faster Predictions in Convolutional Neural Networks

TL;DR

This paper introduces methods to simplify deep CNNs with skip connections into single-layer models for faster predictions, and a training approach that gradually removes skip connections to maintain efficiency.

Contribution

It presents a novel technique to reduce CNN complexity at inference time and a training method to remove skip connections gradually, improving prediction speed without sacrificing accuracy.

Findings

Simplified complex CNNs into single-layer models for faster inference.

Gradual removal of skip connections during training maintains model performance.

Demonstrated effectiveness on Residual Network architectures.

Abstract

Convolutional neural networks (CNNs) are a popular choice of model for tasks in computer vision. When CNNs are made with many layers, resulting in a deep neural network, skip connections may be added to create an easier gradient optimization problem while retaining model expressiveness. In this paper, we show that arbitrarily complex, trained, linear CNNs with skip connections can be simplified into a single-layer model, resulting in greatly reduced computational requirements during prediction time. We also present a method for training nonlinear models with skip connections that are gradually removed throughout training, giving the benefits of skip connections without requiring computational overhead during during prediction time. These results are demonstrated with practical examples on Residual Networks (ResNet) architecture.