What is Max Pooling?
Max pooling is a downsampling technique used in convolutional neural networks (CNNs) to reduce the spatial dimensions of feature maps while preserving the most important information. It is commonly used as a means of reducing the computational complexity and memory requirements of a network during training and inference.
How does Max Pooling work?
Max pooling works by sliding a window (also called a kernel) over the input feature map and selecting the maximum value from the window at each step. This process reduces the spatial dimensions of the feature map while retaining the most significant features, which are assumed to correspond to the maximum values.
Benefits of Max Pooling
Reducing computational complexity: By downsampling the feature maps, max pooling reduces the number of parameters and computations required in a neural network, making it more efficient and faster to train.
Providing translation invariance: Max pooling can help a network become more robust to small translations in the input data by capturing the most important features, regardless of their position within the pooling window.
Preventing overfitting: Due to the downsampling of feature maps, max pooling can also help prevent overfitting by introducing a form of regularization and reducing the number of parameters in the network.
Resources
- Apply 2D max pooling in Pytorch
- LSTM with Two Dimensional Max pooling
- Pytorch Maxpool2D - Worked Example
