  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.
