Between max pooling and average pooling, which is generally considered better for quicker learning in CNNs?

Max pooling is generally considered better for quicker learning in convolutional neural networks (CNNs) due to its ability to help retain the most prominent features from the input data, which are crucial for effectively capturing the essential patterns needed for classification and recognition tasks.

In max pooling, the maximum value from a defined region is used to represent that region, emphasizing the most significant features and effectively reducing the spatial dimensions of the data while preserving important information. This technique can lead to faster convergence during training as the network focuses on the most salient features, facilitating more robust learning.

In contrast, average pooling takes the average of the values from the pooling region. While this approach can smooth out noise and preserve some information, it often leads to the loss of critical features that can define important patterns. This can hinder the learning process, as the model may not capture the essential characteristics of the data effectively.

Overall, the effectiveness of max pooling in retaining significant features contributes to quicker learning in CNNs compared to average pooling.