LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification

TL;DR

This paper introduces LightLayers, a parameter-efficient neural network layer design that reduces training complexity and computational requirements while maintaining competitive accuracy across several image classification datasets.

Contribution

LightLayers, comprising LightDense and LightConv2D, utilize matrix factorization to create lightweight DNN layers that require fewer parameters and less computation.

Findings

Achieved comparable accuracy on MNIST, Fashion MNIST, CIFAR-10 datasets.

Reduced model complexity with minimal accuracy loss on CIFAR-100.

Demonstrated effectiveness of LightLayers in resource-constrained environments.

Abstract

Deep Neural Networks (DNNs) have become the de-facto standard in computer vision, as well as in many other pattern recognition tasks. A key drawback of DNNs is that the training phase can be very computationally expensive. Organizations or individuals that cannot afford purchasing state-of-the-art hardware or tapping into cloud-hosted infrastructures may face a long waiting time before the training completes or might not be able to train a model at all. Investigating novel ways to reduce the training time could be a potential solution to alleviate this drawback, and thus enabling more rapid development of new algorithms and models. In this paper, we propose LightLayers, a method for reducing the number of trainable parameters in deep neural networks (DNN). The proposed LightLayers consists of LightDense andLightConv2D layer that are as efficient as regular Conv2D and Dense layers, but uses less parameters. We resort to Matrix Factorization to reduce the complexity of the DNN models resulting into lightweight DNNmodels that require less computational power, without much loss in the accuracy. We have tested LightLayers on MNIST, Fashion MNIST, CI-FAR 10, and CIFAR 100 datasets. Promising results are obtained for MNIST, Fashion MNIST, CIFAR-10 datasets whereas CIFAR 100 shows acceptable performance by using fewer parameters.