# A Comparison of Pooling Methods for Convolutional Neural Networks
## 6 Discussion  
The following section summarizes the findings of this study, focusing on a review of the pooling techniques and analyses investigated. Pooling helps to learn invariant features, avoid overfitting, and minimize computational complexity by down sampling the extracted features. CNN implements two types of pooling operations: local pooling and global pooling. Local pooling is used to automatically extract features from small image regions (such as 3 \(\times\) 3 pixels) at the start of the CNN. Meanwhile, at the end of the network, we automatically extract features from the full feature map using global pooling. A fully connected layer then uses the feature representation for classification. The two most common pooling techniques are average pooling and max pooling [75]. They are used for local and global pooling layers and their effectiveness is based on the application. Max pooling ignores all other irrelevant activations and only considers the largest element or component within each feature map that is most activated. This activated feature can be noisy. According to our analysis, maximal max pooling should be used when certain class features (such as abnormal regions in medical images) are smaller than the original image. During the network learning stage, the network only updates nodes bound to this largest active element, which only introduces a delay in the learning process. Max pooling is generally used early in the network to capture important local features. This works well if the image is large enough. Our study shows that using max pooling in the early layer of the network leads to information loss and poor classification accuracy when the image size is small. Average pooling, on the other hand, weights each activation equally, regardless of its importance. As a result, background features can dominate the pooled representation and reduce class-specific data in the feature map. A global pooling operator is often used as a pooled average to capture the contribution of all features, such as ResNet. Furthermore, each node in the network is updated during the training phase, which makes the network converge faster. Our analysis further showed that using average pooling as the global pooling operator is preferable to using max pooling. As stated earlier, max pooling or average pooling may not work or be applicable in all cases [76]. Each has its strengths and weaknesses. Mixed max average pooling (Section 2.3) and soft pooling methods are proposed to work around the issues and to gain the benefits of both (Section 2.3). In the max average pool combination, both max and average pools combine weights. A weighted sum of local features is how the pooled representation is generated in soft pooling. Each feature map component contributes to the pooled representation, and their contribution depends on the activation values between these elements. On the other hand, a high activation gives a higher weight and a lower weight. Several other methods of determining these weights have been proposed. Rank-based pooling (discussed in Section 3.4) can also be viewed as a soft pooling method, but with different weights for local features in the pooled representation. In rank-based pooling, the closest k activations are assigned a weight of 1, and the remaining activations are assigned a weight of 0. These strategies (mixed max average, soft pool, and rank-based pools) introduce additional free parameters that must be selected to improve rank performance compared to the max and average pooling. By performing several experiments, researchers have shown that these methods are effective when the size of class-specific characteristics is small compared to the size of the image. In these cases, soft pooling provides better performance than max and average pooling. Additionally, our research shows that average pooling is preferred when class-specific attributes are dispersed throughout images, such as in the medical domain, where we have more abnormal regions than normal ones.