The goal of artificial intelligence is to give robots the ability to think and act like humans. Some fields, such as computer vision or natural language processing, need computers to abandon their basic feature extraction and learning approach in favour of thinking outside the box.
Giving robots the sense of sight and the ability to perceive and explore the world and putting it to use in domains such as image analysis, object recognition, and medical image processing laid the groundwork for the development of a new Deep Learning technique known as Convolution Neural Network.
Introduction to Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have transformed computer vision and image processing. These deep learning architectures have been critical in a variety of applications, including image classification, object identification, facial recognition, and even medical picture analysis.
CNNs, as opposed to typical neural networks, are specially built to handle grid-like input, such as photographs, by utilizing convolutional layers that learn hierarchical features automatically.
How does a Convolution Neural Network work?
Convolutional Neural Networks (CNNs) analyze visual input effectively by exploiting the principle of convolution. The network is made up of layers with learnable filters that move across the input data, extracting essential characteristics via element-wise multiplication and summing.
The collected features are then exposed to non-linear activation functions, which improve the network’s capacity to learn complicated patterns and relationships.
However, Pooling layers reduce spatial dimensions while maintaining vital information and dumping unnecessary components. Based on the learned characteristics, the last layers conduct classification or regression.
CNNs’ hierarchical structure allows them to automatically learn and detect characteristics at many levels of abstraction, making them particularly effective for a variety of computer vision applications.
Components of CNN
Let’s dissect CNN’s architecture and main components:
Layer of Convolution
The convolutional layer is the essential building block of a CNN. It applies to the input picture a collection of learnable filters (also known as kernels).
Each filter is a tiny matrix that is slid over the input picture and performs element-wise multiplication and summing to generate a feature map. Local patterns and structures in the incoming data, such as edges, corners, and textures, are captured by this method.
A convolutional layer’s numerous filters generate several feature maps, each reflecting a distinct element of the input.
Pooling Layer (Subsampling Layer)
Pooling layers are used to reduce the spatial dimensions of feature maps while maintaining the most critical data. Ultimately it a a typical strategy is max pooling, in which a window slides across the feature map and picks the greatest value within that window.
This minimizes the computational effort and makes the network more resistant to minor input translations or distortions.
Fully linked Layer (Dense Layer)
One or more fully linked layers are often added after the spatial dimensions have been lowered. These layers link every neuron from the previous layer to every neuron from the current layer, allowing the network to learn global patterns and make complicated decisions.
In the case of image classification, the final fully connected layer frequently generates the final output, which might be class probabilities.
Softmax Layer
The softmax activation function is frequently used on the output layer in classification tasks. It translates each class’s raw scores into probabilities, showing the chance of the input belonging to each class. The final classification is then projected to be the class with the highest probability.
Loss Function
The loss function computes the difference between the predicted and true labels. Cross-entropy loss is frequently utilized in classification problems. The purpose of the training phase is to minimize this loss by modifying the network’s weights and biases.
Backpropagation and Optimisation
Lastly, CNNs are trained using gradient-based optimization methods like Stochastic Gradient Descent (SGD) or its derivatives. Backpropagation is used to compute the gradients of the loss with respect to the model parameters. The weights are changed repeatedly to minimize the loss function.
Convolutional Neural Network Architectures
CNN architectures are deep-learning models that specialize in computer vision problems. We have discussed some of the most essential ones in depth here.
1. LeNet-5
Yann LeCun created the pioneering Convolutional Neural Network (CNN) LeNet-5 in 1998. It is a watershed moment in the application of CNNs to computer vision. LeNet-5, which was originally designed for handwritten digit identification, effectively proved the capability of convolutional layers in extracting data from photos.
It set the framework for following advances in deep learning by incorporating seven layers, including convolutional, pooling, and fully linked layers. Its resilience and precision in recognising handwritten digits shaped the landscape of contemporary CNN systems. They cleared the door for their extensive use in image-related activities.
2. AlexNet
Next up is AlexNet, a pioneering Convolutional Neural Network (CNN) built by Alex Krizhevsky, who won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012. This watershed event launched the era of deep learning, which has revolutionized computer vision tasks.
AlexNet surpassed standard machine learning approaches because of its novel design of layered convolutional layers, ReLU activation, and GPU-accelerated training.
3. VGG-16 and VGG-19
VGG-16 and VGG-19 are well-known Convolutional Neural Network (CNN) designs created by the University of Oxford’s Visual Geometry Group.
They are differentiated by their homogeneous structure, which emphasizes depth and consists mostly of 33 convolutional layers and 22 pooling layers. These networks highlighted the importance of increasing model depth for enhanced image recognition performance.
Despite their simplicity, VGG-16 and VGG-19 performed admirably in the 2014 ImageNet competition, demonstrating the power of deep CNNs. Their impact on subsequent architectural design, as well as the emphasis on deeper networks, have become critical pillars in current deep learning research.
4. GoogLeNet
GoogLeNet, commonly known as Inception v1, is a game-changing Convolutional Neural Network (CNN) architecture created by Google researchers. It pioneered the “inception module,” which combined different filter sizes (1, 33, and 55) into a single layer.
This effective design decreased the network’s parameters and computational complexity while improving its capacity to capture various characteristics at different sizes.
5. ResNet
Residual Networks is a huge step forward in deep learning. It was proposed by Kaiming who introduced the notion of skip connections or residual blocks, which enabled gradients to travel directly across the network, thus resolving the vanishing gradient problem.
This ground-breaking invention allows the training of incredibly deep CNNs, exceeding prior depth constraints. ResNet provided smoother optimization and considerably increased accuracy by reusing learned features via shortcut connections.
Its launch in 2015 sparked a surge of interest in training dense neural networks, making it a cornerstone of current architecture design and moving the science of computer vision ahead.
6. DenseNet
The notion of dense connection was introduced by DenseNet, a pioneering Convolutional Neural Network (CNN) design. DenseNet, created by Gao Huang, connects each layer to all preceding layers in a feed-forward fashion, resulting in a dense interconnectedness among network blocks. This architecture encourages direct feature reuse and improves network information flow.
7. MobileNet and MobileNetV2
MobileNet and MobileNetV2 are advanced Convolutional Neural Network (CNN) architectures that are specifically developed for mobile and edge devices. These models, created by Andrew G. Howard and Mark Sandler, prioritise lightweight and efficient calculations to meet the constrained resources of mobile platforms.
8. EfficientNet
EfficientNet is a revolutionary Convolutional Neural Network (CNN) architecture that uses compound scaling to balance model size and performance. It was proposed by Mingxing Tan and Quoc V. Le, and produces cutting-edge results by progressively scaling up the network’s depth, breadth, and resolution using a unique compound coefficient.
This novel method optimises the model’s design for varied resource limitations, maximising performance while minimizing the number of parameters. EfficientNet has thereby revolutionized the area of deep learning by producing very efficient and accurate models, making it a go-to choice for a wide range of computer vision applications, even in resource-constrained contexts.
Advantages Of Convolutional Neural Network
Translation Invariance: CNNs are translation invariant, which means they can recognize patterns regardless of where they are in the image. This characteristic is critical for jobs like object detection, where the item’s position may change.
Parameter Sharing: CNNs use parameter sharing in convolutional layers, which greatly reduces the number of parameters when compared to fully connected networks. This increases the memory efficiency of CNNs and allows them to be trained on bigger datasets.
Sparse Connectivity: Each neuron in a convolutional layer is linked to a local portion of the input, resulting in sparse connectivity. This minimizes the computational load and improves the network’s generalization ability.
Pooling for Downsampling: Pooling layers reduce the spatial dimensions of feature maps while keeping critical information. This minimizes computing complexity and aids in the prevention of overfitting.
Transfer Learning: CNNs excel in transfer learning, where pre-trained models on big datasets may be fine-tuned for specific applications with limited data. This is especially beneficial when data availability is limited.
Data Augmentation: CNNs may be utilized with data augmentation techniques to artificially expand the size of the training dataset. This improves generalization and decreases the danger of overfitting.
Real-time Applications: CNNs may be used for real-time applications like autonomous driving, video analysis, and surveillance because of developments in hardware and optimization techniques.
CNN architectures may be tailored and specialized for certain fields like medical imaging, satellite image processing, and creative style transmission.
Applications of Convolutional Neural Networks (CNNs)
Image Classification: CNNs are commonly used for image classification tasks like recognizing items in pictures or medical images, categorizing handwritten digits, and recognizing facial expressions.
Object Detection: CNNs may be used for object detection, which allows for the identification and localization of several items inside an image. This is critical in applications like self-driving cars and surveillance.
Semantic Segmentation: CNNs may segment pictures down to the pixel level, assigning each pixel to a distinct class. This is useful for medical image analysis, interpretation of satellite images, and scene comprehension.
Generation: CNNs are utilized for picture production tasks such as style transfer, super-resolution, and producing realistic images from textual descriptions.
Medical Imaging: CNNs play an important function in medical image processing, supporting radiologists in recognizing medical problems and aiding in an illness diagnosis.
Video Analysis: CNNs are used in applications like video surveillance and entertainment to recognize actions, follow objects in videos, and analyze video information.
Conclusion
In conclusion, Convolutional Neural Networks offer both advantages and limitations, and their usefulness is determined by their unique application and circumstances. Despite their limitations, CNNs have shown to be a breakthrough technique in computer vision, spurring improvements and discoveries across a wide range of areas.
