Enum PoolingType
Defines different methods for pooling (downsampling) data in neural networks, particularly in convolutional neural networks.
public enum PoolingTypeFields
Average = 1Takes the average value from each group of values.
For Beginners: Average Pooling works by looking at small groups of numbers and taking the average (mean) of all values in each group.
Using the same 4×4 grid example:
3 7 5 2 1 4 6 9 2 8 3 5 6 1 4 7
With Average Pooling using 2×2 groups, we'd get:
(3+7+1+4)/4 = 3.75 (5+2+6+9)/4 = 5.5 (2+8+6+1)/4 = 4.25 (3+5+4+7)/4 = 4.75
Which rounds to:
4 6 4 5
Average Pooling is good at preserving background information and overall context. It's like describing a neighborhood by its average house size - you get a general sense of the area rather than just focusing on the biggest houses.
Max = 0Takes the maximum value from each group of values.
For Beginners: Max Pooling works by looking at small groups of numbers and keeping only the largest value from each group.
For example, if we have this 4×4 grid of numbers:
3 7 5 2 1 4 6 9 2 8 3 5 6 1 4 7
And we use Max Pooling with 2×2 groups, we'd get:
7 9 8 7
Max Pooling is especially good at preserving important features like edges and textures. It's like looking at a landscape and remembering only the tallest mountains - you might miss some details, but you'll definitely remember the most prominent features.
Remarks
For Beginners: Pooling is like summarizing information to make it more manageable. Imagine you have a large, detailed photograph and you want to create a smaller version that still captures the important features. Pooling does this for AI models by taking groups of numbers (like pixels) and combining them into single values.
This serves two important purposes:
- It reduces the amount of data the model needs to process, making it faster and more efficient
- It helps the model focus on important features regardless of their exact position (called "positional invariance")
For example, if a model is trying to recognize a cat in a photo, pooling helps it identify cat features whether the cat is in the center, corner, or any other position in the image.