Convolutional Neural Networks (CNNs) have become a cornerstone in the field of computer vision. Advanced CNN structures have been developed to improve accuracy and efficiency in various tasks. This page provides an overview of some of the key advanced CNN structures.
Overview
- Deep Learning: The foundation of CNNs.
- Convolutional Layers: Extract features from the input data.
- Pooling Layers: Reduce the spatial dimensions of the feature maps.
- Fully Connected Layers: Perform classification or regression tasks.
Advanced CNN Structures
1. Inception Networks
Inception networks, introduced by Google, use a multi-scale approach to extract features. They combine different convolutional filters with different kernel sizes to capture various features at different scales.
2. ResNet (Residual Networks)
ResNet introduces the concept of residual learning to address the vanishing gradient problem in deep networks. It uses skip connections to allow gradients to flow directly to earlier layers, enabling the training of very deep networks.
3. DenseNets
DenseNets connect each layer to every other layer in a feedforward fashion, which allows the network to learn more effectively from the input data. This structure helps in reducing the number of parameters and improving the performance.
4. Xception
Xception is a network architecture that builds upon the Inception network. It uses a depthwise separable convolution to reduce the computational cost while maintaining the accuracy.
Further Reading
For more information on advanced CNN structures, you can explore the following resources:
- Deep Learning with Python by François Chollet
- Convolutional Neural Networks by Yann LeCun, Yosua Bengio, and Geoffrey Hinton
If you are interested in learning more about CNNs, you can visit our Introduction to CNNs course.