Computer Science – 18.1 Artificial Intelligence (AI) | e-Consult

Convolutional Neural Networks (CNNs) are a specialized type of ANN particularly well-suited for image recognition tasks. Traditional ANNs often struggle with the high dimensionality and spatial relationships present in images. CNNs address this by leveraging the spatial structure of images and reducing the number of parameters required.

A CNN architecture typically consists of several layers, including convolutional layers, pooling layers, and fully connected layers.

Convolutional layers are the core of a CNN. They use filters (also called kernels) to convolve over the input image. Each filter detects specific features, such as edges, corners, or textures. The filter slides across the image, performing element-wise multiplication with the portion of the image it covers, and summing the results. This produces a feature map that highlights the presence of the detected feature. Multiple filters are used in each convolutional layer to capture a variety of features.

Pooling layers are used to reduce the spatial dimensions of the feature maps, making the network less sensitive to the exact location of features and reducing computational complexity. Common pooling operations include max pooling (selecting the maximum value within a region) and average pooling (calculating the average value within a region).

Finally, fully connected layers are used to combine the features extracted by the convolutional and pooling layers to make a final prediction (e.g., classifying the image into different categories). The learned weights in the convolutional and fully connected layers are adjusted during training using backpropagation to optimize the network's performance on the image recognition task. The use of shared weights in convolutional layers significantly reduces the number of parameters compared to a fully connected network, making CNNs more efficient and less prone to overfitting.