Convolutional Neural Networks (CNNs) are a class of deep neural networks that have been extremely successful in various computer vision tasks. This paper provides an introduction to the fundamentals of CNNs, their architecture, and their applications.
Architecture
CNNs consist of several layers, each of which performs a specific function:
- Input Layer: The input layer receives the raw input data, which is typically an image.
- Convolutional Layers: These layers apply various filters to the input data to extract features such as edges, textures, and shapes.
- Pooling Layers: Pooling layers reduce the spatial dimensions of the feature maps, which helps to reduce computational complexity and prevent overfitting.
- Fully Connected Layers: These layers connect all the neurons from the previous layer to the neurons in the current layer, allowing the network to learn complex patterns and relationships.
- Output Layer: The output layer produces the final prediction, which could be a classification or a regression.
Applications
CNNs have been successfully applied to various computer vision tasks, including:
- Image Classification: Identifying the class of an input image, such as "cat" or "dog".
- Object Detection: Locating and classifying objects within an image.
- Semantic Segmentation: Assigning a semantic label to each pixel in an image.
- Image Generation: Creating new images based on a given input.
Further Reading
For more information on CNNs, please refer to the following resources:
Images
Here are some examples of CNN applications in computer vision: