Convolutional neural networks: what are they and what are they for?

Main

• Convolutional Neural Networks (CNNs) are special types of neural networks that help computers see and understand images and videos.
• Such networks have several layers called convolutional. They allow CNN to learn complex features and make more accurate predictions about the content of visual materials.
• Convolutional neural networks are used, among other things, for face recognition, autopilot, medical prototyping, and natural language processing.

How are convolutional neural networks arranged?

CNNs work by imitating the human brain and use sets of rules that help the computer find features in images and understand and interpret information.

Each layer of such a network processes the data and sends the identified features to the next layer for further processing. They use filters to help highlight important features, such as the edges or shapes of objects in an image.

When filters are applied to visual material, we get a thumbnail image. Then CNN analyzes it and reveals important features. This process is called feature extraction.

In addition to convolutional layers, CNNs include:

• pooling layerswhich reduce the size of the image so that the network can work faster and generalize better;
• normalization layerswhich help prevent overfitting and improve network performance;
• solar layersthat are used for classification.

How do they work?

Convolutional Neural Networks work like this:

• input data such as images or videos are sent to input layer;
• convolutional layers extract various features from the input data. They use filters to detect borders, shapes, textures, and other features;
• after each convolution layer is applied ReLU activation function. It adds non-linearity and helps improve network performance;
• followed by pooling layer. It reduces the dimension of feature maps by choosing the most important values ​​from each area;
• fully connected layers receive output from the pooling layer and use a set of weights for classification or prediction. They combine the selected features and make the final decision.

An example of a task.

Suppose a convolutional neural network needs to classify images of cats and dogs. The operation will be carried out according to the following algorithm:

• input layer: receives color images of a dog or cat in RGB format, where each pixel is represented by the intensities of the red, green and blue color channels;
• convolutional layer: applies filters to an image to highlight characteristics such as edges, corners, and shapes;
• ReLU layer: adds non-linearity by applying the ReLU activation function to the output of the convolutional layer;
• pooling layer: Reduces the dimension of features by choosing the maximum values ​​in each section of the feature map;
• repetition of layers: many convolutional and pooling layers are combined to extract increasingly complex features from the input image;
• smoothing layer: transforms the output of the previous layer into a one-dimensional vector representing all features;
• fully connected layer: takes a smoothed output and applies weights to classify the image as dogs or cats.

A convolutional neural network learns from examples with images that already have labels indicating what is shown. During the learning process, the weights of filters and fully connected layers are changed to reduce the chance of errors between network predictions and correct answers.

Once the training is complete, CNN can determine exactly what is shown in new, yet unfamiliar, images of cats and dogs. She uses her knowledge of features and patterns to make the right classification decision.

What are the types of Convolutional Neural Networks?

• traditional CNNs, also known as “regular”, consist of a series of convolutional and subsampling layers followed by one or more fully connected layers. Each convolutional layer in such a network performs convolutions using trainable filters to extract features from the input image. An example of a traditional CNN is the Lenet-5 architecture, which was one of the first successful convolutional neural networks for handwritten digit recognition. It consists of two sets of convolutional and subsampling layers followed by two fully connected layers. The Lenet-5 architecture has demonstrated the effectiveness of CNNs in image identification, and they have become widely applied in the field of computer vision;
• recurrent neural networks (Recurrent Neural Networks, RNN) — can process sequential data given the context of previous values. Unlike conventional neural networks, which process data in a fixed order, RNNs can operate on inputs of variable length and draw conclusions based on previous inputs. Recurrent neural networks are widely used in natural language processing. When working with texts, they can not only generate text, but also perform translation. To do this, the recurrent neural network is trained on paired sentences composed in two different languages. The RNN processes sentences one at a time, producing an output sentence that depends on the input at each step. Due to this, the recurrent neural network can correctly translate even complex texts, as it takes into account previous inputs and outputs, which allows it to understand the context;
• fully convolutional networks (Fully Convolutional Networks, FCN) are widely used in computer vision tasks such as image segmentation, object detection, and image classification. They are trained end-to-end using backpropagation to categorize or segment images. Backpropagation helps the neural network calculate the gradients of the weight loss function. The loss function is used to measure how well a machine learning model predicts an expected outcome for a given input. Unlike traditional convolutional neural networks, FCNs do not have fully connected layers and are based entirely on convolutional layers. This makes them more flexible and computationally efficient;
• Spatial Transformer Network (STN) – used in computer vision tasks to improve the ability of a neural network to recognize objects or patterns in an image, regardless of their location, orientation, or scale. This is called spatial invariance. An example of using STN is a network that applies a transformation to an input image before processing it. The transformation may include the alignment of objects in the image, the correction of perspective distortions, or other changes that improve the performance of the network in a particular problem. The STN helps the network to process images by taking into account their spatial features and improves its ability to recognize objects in different conditions.

What are the benefits of CNN?

One of the main advantages of convolutional neural networks is shift invariance. This means, as mentioned above, that the CNN can recognize objects in an image regardless of their location.

Another benefit is the shared use of parameters. This means that the same set of parameters is applied to all parts of the input image. This approach allows the network to be more compact and efficient, since it does not have to remember separate parameters for each area of ​​the studied material. Instead, it generalizes knowledge about features across the entire image, which is especially useful when working with large amounts of data.

Other advantages of CNNs include hierarchical representations, which allow complex data structures to be modeled, and resistance to change, which makes them robust for different image conditions. In addition, convolutional networks can be trained end-to-end, that is, the model is trained all the way from input to output, which speeds up the learning process and improves the overall performance of the network.

CNNs can learn different levels of input image features. The upper layers of the network learn more complex features, such as the parts and shapes of objects, while the lower layers learn simpler elements, such as borders and textures. This hierarchical model allows objects to be recognized at different levels of abstraction, which is especially useful for complex tasks such as object detection and segmentation.

In addition, CNNs can be trained on the entire network at once. This means that gradient descent (optimization algorithm) can simultaneously optimize all network parameters to improve its performance and converge quickly. Gradient descent allows the model to adjust parameters based on error information to minimize training loss.

And what are the disadvantages?

CNN training requires a large amount of labeled data and is often time consuming. This is due to the high requirements for computing power.

The architecture of a CNN, including the number and type of layers, can affect network performance. For example, adding more layers can improve the accuracy of the model, but also increases the complexity of the network, and with it the requirements for computing resources. Deep CNN architectures also suffer from overfitting when the network focuses on training data and poorly applies the learned knowledge to new, unknown data.

In tasks where contextual understanding is required, such as natural language processing, convolutional networks can have limitations. For such tasks, other types of neural networks are preferred, which specialize in sequence analysis and take into account contextual dependencies between elements.

Despite these shortcomings, convolutional neural networks are still widely used and show high performance in deep learning. They are a key tool in the field of artificial neural networks, especially in computer vision tasks.

The material was prepared with the participation of language models developed by OpenAI. The information presented here is partly based on machine learning and not real experience or empirical research.

Main

• Convolutional Neural Networks (CNNs) are special types of neural networks that help computers see and understand images and videos.
• Such networks have several layers called convolutional. They allow CNN to learn complex features and make more accurate predictions about the content of visual materials.
• Convolutional neural networks are used, among other things, for face recognition, autopilot, medical prototyping, and natural language processing.

How are convolutional neural networks arranged?

CNNs work by imitating the human brain and use sets of rules that help the computer find features in images and understand and interpret information.

Each layer of such a network processes the data and sends the identified features to the next layer for further processing. They use filters to help highlight important features, such as the edges or shapes of objects in an image.

When filters are applied to visual material, we get a thumbnail image. Then CNN analyzes it and reveals important features. This process is called feature extraction.

In addition to convolutional layers, CNNs include:

• pooling layerswhich reduce the size of the image so that the network can work faster and generalize better;
• normalization layerswhich help prevent overfitting and improve network performance;
• solar layersthat are used for classification.

How do they work?

Convolutional Neural Networks work like this:

• input data such as images or videos are sent to input layer;
• convolutional layers extract various features from the input data. They use filters to detect borders, shapes, textures, and other features;
• after each convolution layer is applied ReLU activation function. It adds non-linearity and helps improve network performance;
• followed by pooling layer. It reduces the dimension of feature maps by choosing the most important values ​​from each area;
• fully connected layers receive output from the pooling layer and use a set of weights for classification or prediction. They combine the selected features and make the final decision.

An example of a task.

Suppose a convolutional neural network needs to classify images of cats and dogs. The operation will be carried out according to the following algorithm:

• input layer: receives color images of a dog or cat in RGB format, where each pixel is represented by the intensities of the red, green and blue color channels;
• convolutional layer: applies filters to an image to highlight characteristics such as edges, corners, and shapes;
• ReLU layer: adds non-linearity by applying the ReLU activation function to the output of the convolutional layer;
• pooling layer: Reduces the dimension of features by choosing the maximum values ​​in each section of the feature map;
• repetition of layers: many convolutional and pooling layers are combined to extract increasingly complex features from the input image;
• smoothing layer: transforms the output of the previous layer into a one-dimensional vector representing all features;
• fully connected layer: takes a smoothed output and applies weights to classify the image as dogs or cats.

A convolutional neural network learns from examples with images that already have labels indicating what is shown. During the learning process, the weights of filters and fully connected layers are changed to reduce the chance of errors between network predictions and correct answers.

Once the training is complete, CNN can determine exactly what is shown in new, yet unfamiliar, images of cats and dogs. She uses her knowledge of features and patterns to make the right classification decision.

What are the types of Convolutional Neural Networks?

• traditional CNNs, also known as “regular”, consist of a series of convolutional and subsampling layers followed by one or more fully connected layers. Each convolutional layer in such a network performs convolutions using trainable filters to extract features from the input image. An example of a traditional CNN is the Lenet-5 architecture, which was one of the first successful convolutional neural networks for handwritten digit recognition. It consists of two sets of convolutional and subsampling layers followed by two fully connected layers. The Lenet-5 architecture has demonstrated the effectiveness of CNNs in image identification, and they have become widely applied in the field of computer vision;
• recurrent neural networks (Recurrent Neural Networks, RNN) — can process sequential data given the context of previous values. Unlike conventional neural networks, which process data in a fixed order, RNNs can operate on inputs of variable length and draw conclusions based on previous inputs. Recurrent neural networks are widely used in natural language processing. When working with texts, they can not only generate text, but also perform translation. To do this, the recurrent neural network is trained on paired sentences composed in two different languages. The RNN processes sentences one at a time, producing an output sentence that depends on the input at each step. Due to this, the recurrent neural network can correctly translate even complex texts, as it takes into account previous inputs and outputs, which allows it to understand the context;
• fully convolutional networks (Fully Convolutional Networks, FCN) are widely used in computer vision tasks such as image segmentation, object detection, and image classification. They are trained end-to-end using backpropagation to categorize or segment images. Backpropagation helps the neural network calculate the gradients of the weight loss function. The loss function is used to measure how well a machine learning model predicts an expected outcome for a given input. Unlike traditional convolutional neural networks, FCNs do not have fully connected layers and are based entirely on convolutional layers. This makes them more flexible and computationally efficient;
• Spatial Transformer Network (STN) – used in computer vision tasks to improve the ability of a neural network to recognize objects or patterns in an image, regardless of their location, orientation, or scale. This is called spatial invariance. An example of using STN is a network that applies a transformation to an input image before processing it. The transformation may include the alignment of objects in the image, the correction of perspective distortions, or other changes that improve the performance of the network in a particular problem. The STN helps the network to process images by taking into account their spatial features and improves its ability to recognize objects in different conditions.

What are the benefits of CNN?

One of the main advantages of convolutional neural networks is shift invariance. This means, as mentioned above, that the CNN can recognize objects in an image regardless of their location.

Another benefit is the shared use of parameters. This means that the same set of parameters is applied to all parts of the input image. This approach allows the network to be more compact and efficient, since it does not have to remember separate parameters for each area of ​​the studied material. Instead, it generalizes knowledge about features across the entire image, which is especially useful when working with large amounts of data.

Other advantages of CNNs include hierarchical representations, which allow complex data structures to be modeled, and resistance to change, which makes them robust for different image conditions. In addition, convolutional networks can be trained end-to-end, that is, the model is trained all the way from input to output, which speeds up the learning process and improves the overall performance of the network.

CNNs can learn different levels of input image features. The upper layers of the network learn more complex features, such as the parts and shapes of objects, while the lower layers learn simpler elements, such as borders and textures. This hierarchical model allows objects to be recognized at different levels of abstraction, which is especially useful for complex tasks such as object detection and segmentation.

In addition, CNNs can be trained on the entire network at once. This means that gradient descent (optimization algorithm) can simultaneously optimize all network parameters to improve its performance and converge quickly. Gradient descent allows the model to adjust parameters based on error information to minimize training loss.

And what are the disadvantages?

CNN training requires a large amount of labeled data and is often time consuming. This is due to the high requirements for computing power.

The architecture of a CNN, including the number and type of layers, can affect network performance. For example, adding more layers can improve the accuracy of the model, but also increases the complexity of the network, and with it the requirements for computing resources. Deep CNN architectures also suffer from overfitting when the network focuses on training data and poorly applies the learned knowledge to new, unknown data.

In tasks where contextual understanding is required, such as natural language processing, convolutional networks can have limitations. For such tasks, other types of neural networks are preferred, which specialize in sequence analysis and take into account contextual dependencies between elements.

Despite these shortcomings, convolutional neural networks are still widely used and show high performance in deep learning. They are a key tool in the field of artificial neural networks, especially in computer vision tasks.

The material was prepared with the participation of language models developed by OpenAI. The information presented here is partly based on machine learning and not real experience or empirical research.