A quick Google search of “data science” will unambiguously reveal to anyone how
popular the field has become in the last five years. Along with data science, artificial
intelligence, machine learning, and deep learning are also doing popular rounds in
the computer science field. The latest to be added to this list is convolutional neural
networks— an innovation from the field of computer vision.

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Where it all started?

Neural networks actually became a hit in 2012 when Alex Krizhevsky won the
ImageNet competition that year. This competition is akin to the Olympics of computer
vision and when Alex used them, the classification error dropped from 26% to 15%.

This was The Unmistakeable Laser Ray of Hope that the companies and computer
scientists needed. Since then, companies like Instagram, Facebook, Pinterest, etc.
have enthusiastically implemented neural networks to provide the best experience to
their audience. Read: Neural Network Tutorial.

The biological connection of convolutional neural networks will also help to make its
foundation clear. In 1962, Hubel and Wiesel showed that different neurons in the
visual cortex were fired only when specific visual cues were present. Together, these
neurons had a columnar structure and when fired, collectively produced visual
perception.

For example, some neurons only fired when they were exposed to horizontal edges.
Others fired in the presence of vertical or diagonal edges. Thus, different neurons
responded to different visual components and enabled us to see.

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What is a Convolutional Neural Network?

A convolutional neural network— also called CNN or ConvNet, is a Deep Learning
algorithm. It takes an input image, assigns weights/ biases to the components of the
image, and then classifies the entire image. With enough training, ConvNets are
capable of learning filters/ classification and the pre-processing required is lower as
compared to other algorithms. Read about differences between deep learning and neural networks.

What we ultimately want a convolutional neural network to do is to differentiate
between images and classify them correctly. It is able to capture both temporal and
spatial dependencies because of the application of relevant filters.

Benefits of Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have been a significant advancement in the field of AI or artificial intelligence in recent years, particularly in deep learning. CNNs have proven incredibly powerful in various computer vision tasks, demonstrating their ability to extract meaningful features from images and significantly improve classification accuracy, object detection, and image recognition systems. Let’s look at the advantages/benefits of CNNs and shed light on their key components, including the fully connected layer.

Understanding Convolutional Neural Networks

Convolutional Neural Networks are a specialized deep learning model designed to process data with a grid-like structure, such as images. They consist of multiple interconnected layers, each responsible for a specific task in the feature extraction and classification process. The fundamental building blocks of CNN in deep learning are convolutional layers, pooling layers, and fully connected layers.

Feature Extraction and Hierarchical Learning

One primary advantage of CNNs is their potential to learn and extract features from images automatically. Convolutional layers apply filters to input images, convolving them with learned parameters to detect relevant patterns and features. This hierarchical learning process allows the network to identify simple features at lower layers, such as edges and textures, and gradually learn more complex features at higher layers, including shapes and objects. This feature extraction capability is critical for achieving high accuracy in various computer vision tasks.

Local Connectivity and Parameter Sharing

Unlike traditional neural networks, CNNs exploit the local connectivity pattern present in images. Each neuron in a convolutional layer processes a small local region of the input image, enabling the network to capture spatial relationships between adjacent pixels. Moreover, parameter sharing is another powerful aspect of CNNs. By using the same set of weights for different locations of the input image, the network can learn to detect a particular feature regardless of its position, enhancing the model’s efficiency and generalization.

The Role of Pooling Layers

Pooling fully connected layer in convolutional neural network play a crucial role in reducing the spatial dimensions of the feature maps while retaining the most important information. They achieve this by down-sampling the feature maps using operations such as max or average pooling. Pooling helps to make the model more robust to variations in the input data, increases its translation invariance, and reduces the computational complexity of subsequent layers.

Fully Connected Layers and Classification

The fully connected layer is the final part of a CNN, responsible for mapping the learned features to the corresponding classes in a classification task. It takes the high-level features extracted by previous layers and connects them to a set of neurons, each representing a specific class label. These neurons use activation functions and softmax normalization to produce the final probabilities for different classes, allowing the network to make accurate predictions.

Convolutional Neural Networks have revolutionized the field of computer vision and deep learning. Their ability to automatically learn and extract meaningful features from images and utilize local connectivity, parameter sharing, and hierarchical learning makes them highly effective in various tasks like image classification, object detection, and image recognition.

The inclusion of fully connected layers at the end of the network facilitates accurate classification by mapping the learned features to specific class labels. With their wide-ranging benefits, CNN in deep learning continue to pave the way for advancements in computer vision and contribute to developing AI systems that can understand and interpret visual data with remarkable precision.

The Basics of How it Works

The image becomes an array depending on the resolution and size of the image.

Each entry in the array will consist of a number from 0 to 255 (if the RGB system is
used). This number will represent the pixel intensity at that point.

Taking all these numbers as input, the computer will output a number. This number
will signify the probability of an image belonging to a certain class (for example house,
road, bus, dog, cat, etc.)

Structure of a CNN

Seeing the above image, you might think there are a lot of layers in a convolutional
neural network, but in reality, there are only 3 major ones. These include:
1. The convolutional layer
2. The pooling layer
3. The fully connected layer
Let’s dive deeper into each one of these.

The convolutional layer

This is the core layer of the convolutional neural network. Its parameters are
composed of a set of filters. These filters are small, but they cover the full depth of the
input volume.

The main task performed at the convolutional layer is the extraction of high-level
features. The first one (as shown in the image above) is responsible for extracting low-
level features like color, edges, etc. The subsequent convolutional layers take out the
high-level features, thus, leading to a complete understanding/ perusal of the image.

The Pooling Layer

This layer is meant to reduce the spatial size of the image representation. As such, it
also helps to reduce the computation and processing amount in the neural network.
Additionally, it also extracts dominant features that are positionally and rotationally
invariant.

One type of pooling is done by using the Max operation. This operation picks the
maximum value from each neuron cluster at the prior layer. The other type of pooling
is the Average pooling which returns an average value from the cluster.
Since Max pooling also acts as a noise suppressant, it performs better than Average
pooling.

As is depicted in the image above, there are multiple pooling layers in addition to
convolutional layers. Greater the number of these layers, the more low-level features
will be extracted. However, computational power expended will also increase.

Now that the image has passed through all the present convolutional and pooling
layers, feature extraction is complete. It is now time for the classification of the image. The Fully Connected Layer carries out this task.

The Fully Connected Layers (FCL)

As the last layer, the FC layer is simply a feed-forward neural network. The input to
the fully connected layer is the flattened output of the last pooling/ convolutional
layer. To flatten means that the 3-dimensional matrix or array is unrolled into a vector.

For each FC layer, a specific mathematical calculation takes place. After the vector has passed through all the fully connected layers, the softmax activation function is used in the final layer. This is used to compute the probability of the input belonging to a particular task.

Thus, the end result is the different probabilities of the input image belonging to different classes.

The process is repeated for different types of images and individual images within those types. This trains the network and teaches it to differentiate between a dog and a cat, and a rose and a sunflower.

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Takeaway

The underlying technology of convolutional neural networks is being continuously refined. The networks are heavily trained so as to output accurate probabilities. It can be rightly said: in the field of computer vision, CNNs spell a revolution alone.

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