Global Average Pooling: A Simplified Way of Feature Extraction
Global Average Pooling (GAP) is a popular operation in the field of computer vision designed to replace fully connected layers in classical Convolutional Neural Networks (CNNs). CNNs are a type of deep learning algorithm used for image recognition, classification, and segmentation tasks.
Traditionally, the final few layers of a CNN consist of a fully connected (FC) layer followed by a softmax activation function. The FC layer takes the output from the preceding convolutional layers and transforms it into a single vector, using a large number of trainable parameters. This vector is then fed into the softmax function, which produces the final output of the network.
GAP, on the other hand, takes a different approach. It generates a feature map for each category of the classification task in the last mlpconv layer. Instead of using a fully connected layer on top of the feature maps, GAP takes the average of each feature map, resulting in a vector that is fed directly into the softmax activation function. This process is more native to the convolutional structure and eliminates the need for a large number of trainable parameters in the fully connected layer.
Advantages of Global Average Pooling
GAP has several advantages over traditional fully connected layers. One significant advantage is that it enforces correspondences between feature maps and categories, making it easier to interpret the results. In other words, the feature maps can be treated as confidence maps for each category.
Another advantage of GAP is that it is more robust to spatial translations of the input. Spatial translations occur when an object within an image is shifted to a different location, but the object itself remains the same. GAP takes the average over the entire feature map, effectively summing out the spatial information. This process makes the network more robust to spatial translations and reduces the likelihood of overfitting.
The most significant advantage of GAP is that there are no parameters to optimize. Since the operation only takes the average over the feature map, there are no trainable parameters in the GAP layer. This feature means that overfitting can be avoided at this layer, and the network is less prone to errors caused by high variance.
Applications of Global Average Pooling
GAP has become a standard operation in many state-of-the-art deep learning networks, including Google's Inception V3, Resnet, and VGGNet. These networks use GAP as a way of reducing the number of trainable parameters required for classification tasks, making them more efficient.
GAP has also been used in the field of medical imaging, particularly in the classification of medical images. For example, GAP has been used in the classification of diabetic retinopathy, a condition that affects the eyes of people with diabetes. In this case, the GAP layer was used to classify the severity of diabetic retinopathy based on retinal images.
Another medical application of GAP is in the classification of breast cancer. Researchers used GAP in combination with transfer learning to analyze mammograms for breast cancer detection. The researchers used a pre-trained model on a large dataset, consisting of mammograms from thousands of patients, to fine-tune their network to the specific classification task. The results showed that the network using the GAP operation achieved better accuracy than the traditional fully connected layer network.
Global Average Pooling is an operation in deep learning that takes the average of each feature map and feeds it directly into the softmax activation function. GAP has several advantages over traditional fully connected layers, including better interpretation of results, robustness to spatial translations, and avoidance of overfitting. GAP has become a standard operation in many state-of-the-art deep learning networks, making them more efficient. GAP also has applications in the field of medical imaging, particularly in the classification of diabetic retinopathy and breast cancer detection. Overall, GAP is a simplified and efficient way of feature extraction in deep learning networks, providing better accuracy while requiring fewer parameters and less risk of overfitting.
