What is Convolutional Neural Network (CNN)? Definition, Working Principles, and Main Applications – AI Encyclopedia Knowledge

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What is Convolutional Neural Network?
Convolutional Neural Network (CNN) is a type of deep learning algorithm mainly used in the field of computer vision, which has applications in various fields, including image and video recognition, natural language processing, and even gaming. CNN has completely revolutionized the field of computer vision, providing state-of-the-art performance in tasks such as object detection, image segmentation, and facial recognition. In this article, we will briefly introduce the internal working principle, architecture, and real-world applications of CNN.
The Principles of Convolutional Neural Networks
To understand CNN, one must be familiar with the basic concepts of neural networks. A neural network is a computational model inspired by the structure and function of the human brain, consisting of interconnected artificial neurons. These neurons are organized into layers, each receiving input from the previous layers and sending output to subsequent layers.
CNN is a specialized type of neural network that focuses on processing data with a grid like structure, such as images. The main component of CNN is the convolutional layer, which aims to automatically and adaptively learn spatial hierarchical features from input data.
Convolutional layer
Convolutional layers are the core part of CNN. It performs convolution operation, which is a mathematical operation that takes two functions as inputs and produces a third function as output. In the context of CNN, the input function is usually an image and a filter (also known as the kernel). Convolutional operation is used to analyze local patterns in the input image by sliding the filter over the image and calculating the dot product between the filter and the image area it covers.
This process generates a feature map, which is a representation of the input image, highlighting the areas where specific features are detected by the filter. By using multiple filters in the convolutional layer, CNN can learn to recognize different features in the input image.
Typical CNN Structure By Aphex34- Own Work, CC BY-SA 4.0
Pooling layer
Pooling layers are another important component of CNN. They are used to reduce the spatial size of feature maps generated by convolutional layers. The main goal of the pooling layer is to reduce the computational complexity of the network while maintaining the most relevant features.
There are several types of pooling operations, with the most common being max pooling. In the maximum sink, a window (usually 2×2) slides over the feature map, and the maximum value within the window is selected as the output. This operation effectively reduces the spatial size of the feature map while preserving the most important features.
Fully connected layer
After a series of convolutional and pooling layers, the last layer of CNN is usually the Fully Connected Layers. These layers are responsible for generating the final output of the network. They tile the feature maps generated from the previous layers into a single vector. Then, this vector is fed into a standard feedforward neural network, which can be trained to produce the desired output, such as classifying the input image into different categories.
Training of Convolutional Neural Networks
CNN is trained using supervised learning methods, and the network is provided with labeled training data. The training process involves adjusting the weights and biases of filters and neurons in the network to minimize the difference between the predicted output and ground truth labels. This is usually a variation of gradient descent optimization algorithms, such as stochastic gradient descent or Adam optimizer, to accomplish.
During the training process, the network learns to detect layered features in the input data. Lower layers learn simple features such as edges and corners, while higher layers learn more complex features such as shapes and textures.
The Application of Convolutional Neural Networks
CNN has found widespread applications in various fields, with some of the most prominent applications including:
Image classification: CNN demonstrates excellent performance in image classification tasks, with the goal of assigning input images to one of several predefined categories.
Object detection: CNN is used to detect and locate multiple objects in an image, providing category labels and bounding boxes for the detected objects.
Image segmentation: In image segmentation tasks, CNN is used to segment an image into multiple parts, each corresponding to a specific object or region of interest.
Facial recognition: CNN has become the main technology of modern facial recognition systems, providing accurate recognition and verification based on individual facial features.
Natural language processing: Although mainly used for computer vision tasks, CNN has also found applications in natural language processing tasks such as sentiment analysis and document classification.
Convolutional neural networks have had a significant impact on the field of computer vision and other fields, providing state-of-the-art performance in various tasks. By leveraging the power of hierarchical feature learning, CNN has developed advanced applications in image recognition, object detection, facial recognition, and natural language processing. With the continuous deepening of research in the field of deep learning, we can look forward to the further development and new applications of CNN in the future, ultimately improving human ability to process and understand complex data.

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