Convolutional Neural Networks

 

An introduction to Convolutional Neural Networks

 

Introduction: What is an artificial neural network?

In recent times, words like deep learning, machine learning and artificial intelligence have become so common that even school kids are now somewhat familiar with these terms. The advent of Machine Learning has been followed by the rise of Artificial Neural Networks (ANNs). ANNs are computational processing systems used to handle a large amount of data. They take inspiration from the way biological nervous systems operate. These neural networks have a number of hidden layers stacked upon each other. The basic computational units of a neural network are called neurons. Just like neurons in the human brain, these fundamental blocks take in input signals, process them and produce the output. Figure 1 below shows the basic structure of any ANN architecture:

Figure 1

What are convolutional neural networks?

In this article, we will focus on Convolutional Neural Networks (CNNs). CNNs are similar to ANNs, but they are used to perform tasks such as image processing and pattern recognition within images. An image is nothing but a two dimensional signal that can be represented in the form of a matrix. While setting up the CNN architecture, one must take into consideration the fact that the input to such systems consists of images.

 

Methodology:

A CNN comprises three important layers which can be stacked together to form the CNN architecture. These are convolutional layers, pooling layers and fully connected layers.

 

1.  Convolutional layer

This layer is based on the linear mathematical operation of convolution in which two signals are multiplied to produce a third signal. When data hits this layer, convolution takes place between the input and a filter of particular size. The output of this layer is in the form of a 2D activation map which gives information about the image itself.

2.  Pooling layer

The convolutional layer is followed by a pooling layer. The primary aim of this layer is to reduce the computational complexity of the model and to make it more cost effective. Thus the pooling layer is destructive in nature and reduces the dimensionality of the representation.

3.  Fully connected layer

The fully connected layer consists of neurons that are connected directly to the neurons of the two adjacent layers. In the previous layers, the input image is flattened and fed to the fully connected layer. In this layer, mathematical functions operate and classification of the image takes place. 

 

Figure 2 below shows the structure of the layers of a CNN which have been described above.

Figure 2

 

Observation:

Despite the fact that CNNs require a relatively small number of layers,there is no set way for formulating a CNN architecture. The common architecture includes stacking of convolutional layers, followed by pooling and fully connected layers. Another practice is to stack multiple convolutional layers before the pooling layer so as to handle more complex features. Also to reduce the computational complexity, large convolutional layers are split into smaller ones. CNNs are very powerful machine learning algorithms but can be resource heavy too. Hence to address this problem, the spatial dimensionality of the input images is reduced.

 

Conclusion:

Thus to conclude, we can say that convolutional neural networks focus on only a specific type of input and hence, it is easier to set up the architecture for the same. Applications of CNNs include research in the field of image analysis which range from image and video analysis, medical image processing, image classification and computer vision.

 

References:

1.  https://www.researchgate.net/publication/285164623_An_Introduction_to_Convolutional_Neural_Networks

2.  https://www.upgrad.com/blog/basic-cnn-architecture/

Written by,

Mugdha Deshpande

SY EnTC