Exploring ConvNets: A Simple Guide to Understanding Convolutional Neural Networks

AI is evolving at a very fast pace, improving the interaction between the human and the artificial world. This progress is seen in different subfields of the field, with Computer Vision as one of the most representative.

Computer Vision is the field that tries to teach machines to see by providing them with the ability to understand what they see in the same manner as humans do.

This capability ranges from image and video recognition, image analysis, media recreation, and even complex systems such as recommendation engines, and natural language processing.

One of the main enablers of these developments is the Convolutional Neural Network, an algorithm that has been improved over the years to increase the efficiency of Computer Vision solutions.

Diving Deeper into Convolutional Neural Network

A Convolutional Neural Network (CNN or ConvNet) is a deep learning model that is specifically developed to handle image data. It can process an input image by associating learnable weights and biases to different elements or objects in the image and thus separate them.

As compared to other classification algorithms, CNNs need much less pre-processing. In traditional methods, filters are designed by hand while in CNNs, the network can learn these features provided that it is trained sufficiently.

The structure of CNN is similar to the neuron connectivity in the human brain, and it is inspired by the visual cortex. In this arrangement, one neuron fires in response to stimuli only within the receptive field of vision part of the visual field. These fields interconnect to provide a broad coverage of the whole visual region.

Investigating CNN Architectures

The study of convolutional neural networks (CNNs) has been done for a long time, with early work done by Kunihiko Fukushima in 1980 and Yann LeCun in 1989.

Fukushima started the early work on CNN and LeCun’s “Backpropagation Applied to Handwritten Zip Code Recognition” was another step ahead in the CNN advancement for recognizing the pattern in the handwritten zip code.

LeCun’s work during the 1990s, including the creation of “LeNet-5” for document recognition, established the foundation for the present-day CNNs.

The field has since then experienced significant growth, especially with the use of different large datasets including MNIST and CIFAR-10, and competitions like the ILSVRC. This environment spurred the creation of several influential CNN architectures, such as:

• AlexNet
• VGGNet
• GoogLeNet
• ResNet
• ZFNet

Of these, LeNet-5 is still one of the most important ones, and it is considered to be the classic CNN architecture.

How Does a Convolutional Neural Network Work?

Among different types of neural networks, Convolutional Neural Networks (CNNs) are especially effective in image, speech, or audio signal processing. These networks primarily consist of three types of layers:

• Convolutional Layer
• Pooling Layer
• Fully-Connected (FC) Layer

Convolutional Layer

The convolutional layer is one of the key layers in Convolutional Neural Networks (CNNs) and is charged with most of the computation. This layer takes input data usually in the form of a colour image which is a 3D matrix of RGB pixels and applies filters to it and generates feature maps.

The operation includes a feature detector or kernel, which is commonly a 3x3 matrix, moving across the image’s receptive fields in a method called convolution. This interaction produces a set of outputs (dot products) that create a feature map that holds certain features from the image.

Pooling Layer

After the convolutional layer, the pooling or downsampling layer is used to downsample the received feature maps and make the information received simpler and with fewer parameters.

This layer sums the values in its receptive field by using a filter but this filter does not contain weights as in the convolution. There are two main types of pooling operations:

• Max Pooling: This method chooses the maximum value from the receptive field and enhances the features of the strong ones.
• Average Pooling: This approach takes the average of the values in the receptive field making the features smoother.

Pooling aids in making the feature detection process scale and orientation invariant and also saves computation for the next layers.

Fully-Connected (FC) Layer

The fully-connected layer is the last in the network architecture and it acts as the classifier.

The FC layer then deciphers these features to categorize the input data into different classes depending on the learned weights from the convolution and pooling layers.

This layer is fully connected to the input layer and output layer with the use of the softmax activation function to provide the probability of the network’s prediction.

The last layer is the fully-connected layer that combines the spatially hierarchical features obtained by the previous layers for making the final decision, which is very essential for tasks such as image classification.

Pros of Convolutional Neural Networks

Here are the advantages of CNNs:

• Pattern Recognition: Excellent in feature and pattern detection in images, videos, and audio.
• Invariance: It is translation, rotation, and scale invariant.
• Streamlined Training: Enables training from scratch to end without the need for feature engineering.
• Scalability: Able to process big data and it has high accuracy rates.

Disadvantages of Convolutional Neural Networks

CNNs also have some disadvantages:

• Resource Intensive: Needs a lot of computational resources for training and storage of the trained model.
• Risk of Overfitting: Lacks generalization ability if not trained with enough data or when not properly regularized.
• Data Dependency: Requires a large amount of data that are labeled correctly.
• Limited Interpretability: It is challenging to comprehend the internal processes and what the network has learned.

The Relevance of Convolutional Neural Networks (CNNs)

CNNs are very effective in extracting and learning relevant features from image and time series data making them very useful in different technological fields. Here’s how CNNs are employed across different fields:

Medical Imaging

In the field of medical diagnosis, CNNs examine large sets of pathology images to determine whether there are cancer cells or not, thus improving the speed and effectiveness of medical examinations.

Audio Processing

CNNs are very important in audio signal processing, especially in voice-activated systems. They allow devices to understand particular spoken keywords (for example, ‘Hey Siri!’) by training the device to recognize these words correctly while rejecting all other sounds.

Object Detection

In the automotive industry, CNNs are essential for the implementation of ADSs. They identify road signs and other objects that help in the safe driving and decision-making for self-driving cars.

Synthetic Data Generation

CNNs are also used in the generation of synthetic data by means of Generative Adversarial Networks (GANs). This capability is crucial for creating and improving facial recognition algorithms and improving AI-based driving systems by offering various and realistic datasets.

Non-Linearity Layers in Image Processing

Images have non-linear patterns and hence when using convolutional layers which are linear in image processing, we usually include non-linearity layers. These layers assist in transforming the simple outputs into forms that are closer to the nature of images.

Here’s a breakdown of the most common non-linear functions used:

Sigmoid Function

How it Works: Transforms any real number into a number between 0 and 1.

Formula: σ(x) = 1 / (1 + e^(-x))

Drawbacks: It can be close to zero over large parts of its domain, and this is a problem during backpropagation because gradients may vanish. This effect can halt the learning process of the network or slow it down.

Tanh Function

How it Works: Like the sigmoid but scales the output between -1 and 1, which is symmetric about zero.

Benefits: As opposed to sigmoid, having outputs that are centered around zero can be beneficial when it comes to training speed.

ReLU (Rectified Linear Unit)

How it Works: It eliminates all negative values and retains positive values as they are.

Formula: f(x) = { (x, if x ≥ 0) 0, otherwise

Advantages: In general, it enables the training to be faster and is less prone to the vanishing gradient problem than sigmoid and tanh.

Challenges: Can be delicate during training; an improperly set learning rate can cause some neurons to stop updating at all.

Each of these non-linear layers has its own properties and problems, but they are necessary to capture the non-linear relationships in the image data.

Conclusion

As highlighted in this guide, Convolutional Neural Networks (CNNs) are central to enhancing the performance of artificial intelligence especially in the area of computer vision.

On the one hand, the benefits of CNNs are undeniable, which makes them crucial in tech-oriented industries, on the other hand, there are some difficulties.

However, these drawbacks do not hinder the advancement and improvement of CNN architectures since the capability of machines to understand and interpret images is still being expanded.

The push towards more efficient, flexible, and resilient networks guarantees that CNNs will remain one of the most active areas of AI research and development.

For more information on the functions and importance of these networks, contact AllianceTek developers today.