Convolutional Neural Network: operation, advantages and applications in AI
Convolutional Neural Networks (CNN) are powerful tools in artificial intelligence. They are a subcategory of machine learning and are used to improve the generalization performance of learning algorithms. Convolutional neural networks, as a subcategory of machine learning, find applications in image recognition, recommendation systems, and Natural Language Processing. They are particularly effective for processing visual data. Originally developed for image recognition, CNNs quickly found applications in various fields.
A convolutional neural network is a deep neural network architecture. It is distinguished by its ability to extract relevant characteristics from images, thanks to its convolutional layers. These networks mimic the functioning of the visual cortex of animals.
CNNs are used for image classification, object detection And image segmentation. They offer superior performance compared to other image processing methods. In addition to computer vision research, CNNs are also being applied in fields like medical diagnostics, automotive, and many others. Curious to know more? We Tell You Everything!
What is a convolutional neural network (CNN)?
A convolutional neural network (CNN) is a type of artificial neural network specially designed to process and analyze visual data. Inspired by the organization of the visual cortex in animals, CNNs are particularly effective for image recognition and visual analysis tasks.
CNNs stand out from other neural networks because of their unique architecture. They use convolution layers, pooling layers, and fully connected layers. The pooling layer reduces the dimensionality of the data by maintaining only the most important characteristics, which limits the Over-Apprenticeship. There are various types of pooling, such as Max Pooling And theAverage Pooling, each with its pros and cons.
Fully connected layers perform high-level reasoning in the neural network by connecting each node in the output layer to a node in the previous layer. They generally use an activation function. Softmax To classify the entries accordingly, producing a probability of 0 to 1.
Here are the three main components of CNNs:
Convolutional layers
Convolutional layers are the core of convolutional neural networks. Their main function is to extract characteristics from the input data, usually images. They have various functions, among others:
- Convolutional filtering : Convolution layers apply filters (or kernels) to the input image. A filter is a small matrix, often 3x3 or 5x5 in size, that passes (or “convolves”) the image.
- Characteristic detection: Each filter detects different types of features, such as specific edges, textures, or patterns. For example, one filter may detect horizontal edges, while another may detect vertical edges.
- Feature maps: The result of applying a filter to the image is a feature map. Each convolution layer produces multiple feature maps, corresponding to each filter used.
- Nonlinearity: After applying the filter, a nonlinear activation function, such as ReLU (Rectified Linear Unit), is often applied to introduce nonlinearity into the model. This makes it possible to capture more complex relationships in the data.
Diapers of Pooling
The Diapers of Pooling, also called subsampling or subnetworks, are used to reduce the dimensionality of feature maps while maintaining important information. The pooling layer reduces the dimensionality of the data by maintaining only the most important characteristics, which limits over-learning. This helps to decrease the number of parameters and to reduce the risk of overlearning. There are two types of Pooling, including:
- Max-Pooling : It is the most common pooling method. It divides the image into sub-regions that are not superimposed and takes the maximum value for each sub-region. For example, in a 2x2 region, the Max Pooling Will take the highest value of the four pixels.
- Average-Pooling : which is another common method where the average of the values in each subregion is calculated. This method is less aggressive than max-pooling but maintains less detail.
The Pooling Reduces the size of feature maps, which reduces the number of parameters and calculations required in the network. This helps to make the model more effective (and, it cannot be said enough, less subject to overfitting or overlearning!).
Fully Connected Diapers
Fully connected diapers (Fully Connected Layers) are generally found at the end of a CNN and serve as a classifier for the characteristics extracted by the previous layers. These layers are used for high-level reasoning in a neural network, by exploiting activation functions like Softmax for classification. These layers generally use an activation function. Softmax To classify the entries accordingly, producing a probability of 0 to 1. These layers have different functions:
- Full connection: In these layers, each neuron is connected to all the neurons in the previous layer. This allows the extracted characteristics to be combined to form an overall representation of the image.
- Classification: Fully connected layers take learned characteristics and turn them into final outputs. For example, for an image classification task, the output would be a probability vector representing the various possible classes.
- Activation function: Neurons in these layers often use activation functions like Softmax for multi-class classification problems. The function Softmax Converts values into probabilities, making it easier to interpret the results.
- Learning to use weights: During training, the weights of these connections are adjusted to minimize prediction error. Fully connected layers play a key role in generalizing the model to unseen data.
💡 In summary, Convolutional neural networks combine these three types of layers to process images hierarchically. The convolutional layers extract local characteristics, the layers of Pooling Reduce dimensionality and fully connected layers classify extracted features. This architecture allows CNNs to achieve exceptional performance in numerous computer vision tasks and other areas of artificial intelligence.
How does a convolutional neural network work?
The functioning of a convolutional neural network (CNN) is based on an architecture composed of several types of layers (in the three layers mentioned above) that work together to extract characteristics from images and perform tasks such as classification or object detection. Here is a detailed description of the end-to-end process.
Image preprocessing
Before being introduced into a convolutional neural network (CNN) and undergoing the three layers mentioned above, an image must go through preprocessing to ensure that the data is in an optimal format for learning. Typical image preprocessing steps include:
1. Resize
Images can vary in size, but CNNs often require that all input images be the same size. As a result, each image is resized to a standard size, such as 224x224 pixels for some common models.
2. Normalization
Normalization involves adjusting pixel values to be within a common range, often between 0 and 1 or -1 and 1. This helps to accelerate convergence during training and to improve the stability of the model.
3. Centering and Calibration
For some applications, it may be useful to center the data around zero by subtracting the mean from the pixel values and dividing by the standard deviation.
4. Increase in data
Data augmentation involves applying random transformations to the training image to create variations. This helps to make the model more robust by teaching it to recognize objects despite possible variations. Common techniques include:
- Rotation
- zoom
- Flip
- Change in brightness and contrast.
Pre-processing images is an important step in the process, as it ensures that all images are similar in size and format, making it easy for the model to learn. Normalizing and centering data helps to stabilize training and accelerate convergence. In addition, the increase in data allows the model to generalize better by learning from larger variations in training data.
Training and learning
The training of a convolutional neural network (CNN) is based on backpropagation. Neural networks are a subset of machine learning, and they play a key role in deep learning algorithms. Machine learning is used to improve generalization performance and combat overlearning in convolutional neural networks. It is an iterative process that adjusts network weights to minimize a loss function that describes the discrepancy between model predictions and the actual values of the training data.
Backpropagation
The first step in backpropagation is to calculate the loss (or error) between the network predictions and the actual values of the training data. This loss is measured by a loss function appropriate to the problem, such as cross-entropy for classification or mean squared error for regression.
For example, in the case of classification, if a model predicts a probability of 0.8 for the correct class and the Ground Truth (label) is 1 (positive class), the loss could be calculated as -log (0.8), according to the cross-entropy formula.
Once the loss is calculated, the downward gradient algorithm is used to adjust the network weights to minimize this loss. The gradient of the loss function with respect to each weight in the network is calculated using backpropagation, which propagates the error from top to bottom through the network. Here is the process for updating the weights:
- Gradient calculation : The gradient of the loss function with respect to each weight is calculated using the partial derivation.
- Weight update : The weights are updated in the opposite direction to the gradient, which adjusts them to reduce the loss.
- Apprenticeship rate : A learning rate is used to control the size of the update steps. A lower learning rate can help converge more slowly but more stably. On the other hand, a higher learning rate may accelerate convergence, but may jump above the overall minimum.
This process of calculating the loss and updating the weights is repeated for each sample in the training data set over several iterations called “epochs.” At each era, network weights are adjusted to better represent training data and reduce overall loss.
Training a CNN is critical because it allows the model to learn from training data and generalize to new data not seen. By adjusting the weights of the network through backpropagation, CNN learns to recognize patterns and characteristics in data. This allows it to make accurate predictions about new entries.
Optimization and regularization
During convolutional neural network (CNN) training, various optimization and regularization techniques are used to improve learning efficiency and to prevent overlearning. Here are the most frequently used techniques:
1. Optimizers
Optimizers are algorithms that adjust network weights during training in order to minimize loss function. They allow you to control the speed and direction of weight updates. Here are some of the optimizers that are commonly used:
- Adam (Adaptive Moment Estimation): A popular optimization algorithm that adapts the learning rate for each parameter based on the moving average of the gradients and the moving average of the squares of the gradients.
- rmsProp (Root Mean Square Propagation): Another optimization algorithm that adapts the learning rate for each parameter by dividing the learning rate by the square root of the moving average of the squares of the gradients
2. Regularization
Regularization is a technique used to prevent overlearning by limiting the complexity of the model. It aims to make the model more generalizable by reducing unwanted noise variations in training data. Two of the most commonly used regularization techniques are:
- Droput : During training, neurons are randomly dropped with a certain probability (generally between 0.2 and 0.5) at each iteration. This forces the network not to rely too much on particular neurons, which reduces the risk of overlearning.
- L2 regularization: Also called weight adjustment, it adds a penalty to the loss function by adding the sum of the squares of the weights in the model. This pushes the weights to smaller values, thereby reducing model complexity and the susceptibility to overfitting.
Optimization and regularization techniques are essential to form effective and generalizable CNNs. They help avoid problems such as overfitting, where the model fits the training data too precisely and does not generalize well to the new data. By applying these techniques, CNNs are able to learn representative models of data and to make accurate predictions about unknown data.
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Why are convolutional neural networks important for computer vision?
Convolutional neural networks (CNNs) are of paramount importance for computer vision for several reasons:
Automatic feature extraction
Convolutional neural networks (CNNs) are capable of automatically learning characteristics at various scales and levels of abstraction directly from input data.
Unlike traditional methods where feature descriptors were designed manually, CNNs can learn to extract relevant patterns and structures from data without requiring specific human expertise.
This greatly simplifies the process of developing models in computer vision, allowing researchers and engineers to focus more on problem formulation and optimizing network architectures.
Hierarchy of characteristics
CNNs learn characteristics in a hierarchical manner, allowing them to capture information at various levels of abstraction. In the initial layers, convolution filters detect simple patterns such as edges, textures, and colors.
As information is propagated across the network, the higher layers combine these simple patterns to detect more complex features, such as shapes, patterns, and objects.
This hierarchy of characteristics is critical for recognizing and understanding objects in images because it allows the network to represent data in a more discriminating and informative manner.
Robustness to variations
CNNs are inherently robust to variations in data, such as changes in scale, rotation, and translation. This robustness derives from the structure of CNNs and their convolution and Pooling, which allows the network to detect patterns regardless of their exact position in the image.
In addition, regularization techniques such as Dropouts and L2 regularization help prevent overlearning, which further enhances CNNs' ability to generalize effectively to new data.
Ability to process high resolution images
CNNs are capable of processing high-resolution images efficiently by reducing the dimensionality of the data while maintaining relevant information.
The operations of Pooling and convolutional layers allow the network to reduce the spatial size of representations while maintaining important characteristics. This allows CNNs to process images of various sizes and resolutions without compromising model performance, which is crucial in many practical computer vision applications.
Outstanding performances
CNNs have demonstrated exceptional performance in a wide variety of computer vision tasks. They have significantly exceeded traditional methods in tasks such as image classification, object detection, semantic segmentation, facial recognition, and many more.
Their ability to learn discriminative characteristics from data and to generalize effectively to new data makes them powerful tools for solving complex problems in computer vision.
Thus, they pave the way for numerous innovative applications in areas such as health, safety, automotive and many others.
What is the importance of convolutional neural networks in deep learning?
Convolutional neural networks (CNNs) are of paramount importance in the field of Deep Learning for several reasons:
Efficient processing of visual data
CNNs introduced a major advance in visual data processing by allowing computers to perceive and analyze images in a manner similar to that of humans.
Their architecture is specially designed to detect visual patterns at various scales and levels of complexity. They are then particularly suited to computer vision tasks such as classification, object detection, and semantic segmentation.
With their ability to learn characteristics directly from data, CNNs can automatically extract relevant information. This, without requiring manual feature engineering, which greatly simplifies the model development process.
Hierarchy of characteristics
CNNs Learn Characteristics in a Hierarchical Manner by Stacking Multiple Layers of Convolution and Pooling.
The first few layers learn simple characteristics such as edges and textures. As for the deeper layers, they teach more abstract and complex characteristics, such as shapes and patterns.
This hierarchy of characteristics allows CNNs to represent data effectively with varying levels of abstraction. This is essential for recognizing and understanding objects in images.
Robustness to variations
CNNs are inherently robust to variations in data. This means they can effectively generalize to data that has variations such as changes in scale, rotation, and translation.
This robustness is due to the local nature of convolution and pooling operations, which allow the network to detect patterns regardless of their exact position in the image.
In addition, CNNs are able to learn representations that are invariant to transformations, making them even more resistant to variations in the data.
Reduction in compute overhead
CNNs reduce computational overhead compared to fully connected neural networks by sharing the weights of convolutional filters and using pooling operations to reduce the dimensionality of feature maps.
This more efficient architecture allows CNNs to process large amounts of data more quickly and with fewer computing resources. Thus, they are particularly suitable for practical applications on a large scale.
Knowledge transfer
CNNs pre-trained on massive data sets like ImageNet capture general image characteristics that are useful for many computer vision tasks.
These pre-trained models can be used as a starting point for specific tasks with smaller data sets, where they are Fine-Tunés To adapt to the specific characteristics of the data for the task in question.
This knowledge transfer approach makes it possible to build efficient models with less training data. This is especially beneficial in cases where data sets are limited or expensive to obtain.
What are the concrete use cases of CNNs and in which sectors?
Convolutional neural networks (CNNs) have a diverse range of concrete use cases across many industries. Here are some representative examples:
Computer Vision and Image Processing
- Image classification : CNNs are used to classify images into various categories, such as classifying animal species, recognizing objects in images, or classifying diseases based on medical images.
- Object detection : CNNs makes it possible to detect and locate specific objects in images, which is used in security surveillance, autonomous driving, and robotics.
- Image segmentation : CNNs are used to segment images into regions of interest, which is useful in fields such as medicine for segmenting tissues and organs in medical images.
Automotive and smart transport
- Autonomous driving : CNNs are used in autonomous vehicle perception systems to detect pedestrians, vehicles, traffic signs, etc., for safe and autonomous driving.
- Traffic analysis : CNNs are used to monitor and analyze road traffic, making it possible to predict congestion, optimize routes, and manage traffic effectively.
Medicine and Health
- Medical imaging : CNNs are used to analyze medical images such as X-rays, MRIs, and CT scans to detect abnormalities and diagnose diseases.
- Disease detection : CNNs are used to identify symptoms and signs of illness from clinical data and medical images, allowing for early and accurate diagnosis.
Surveillance and security
- Video surveillance : CNNs are used to monitor environments in real time, detecting suspicious behavior, intrusions, or anomalous events.
- Anomaly Detection : CNNs are used to detect anomalies in sensor data, industrial systems, or processes, helping to prevent failures and optimize operations.
E-commerce and recommendation
- Visual search : CNNs are used to improve visual search systems, allowing users to find similar products based on an image.
- Product recommendation : CNNs are used to recommend products based on user preferences and product characteristics, by analyzing images and other relevant data.
Entertainment and Games
- Video games : CNNs are used to create more realistic gaming environments, improving graphics quality and making interactions more natural.
- Multimedia content analysis : CNNs are used to analyze multimedia content, identifying objects, people, or actions in videos and images, which is useful for content recommendation and media curation.
Conclusion
In conclusion, convolutional neural networks (CNNs) represent a major advance in the field of artificial intelligence, offering remarkable capabilities for solving complex problems in various fields.
Their architecture inspired by the functioning of the human brain allows them to automatically learn visual representations from raw data. They are therefore particularly effective for tasks such as computer vision, image processing and pattern recognition.
However, despite their successes and potential, CNNs are not without challenges. Issues such as the interpretability of models, the robustness to adversaries, and the ethics of their use continue to generate debate and research.
Moreover, the steady progress in the field of artificial intelligence is paving the way for new architectures and techniques that could complement or even replace CNNs in the future.
