What is Generalized Mean Pooling?
Generalized Mean Pooling (GeM) is a mathematical operation used in deep learning to compute the generalized mean of each channel in a tensor. It is a generalization of the average pooling, which is commonly used in classification networks, and of spatial max-pooling layer. By applying GeM, it is possible to increase the contrast of the pooled feature map and focus on the salient features of the image.
How Does Generalized Mean Pooling Work?
The generalized mean pooling function can be defined mathematically as:
$$ \textbf{e} = \left[\left(\frac{1}{|\Omega|}\sum\_{u\in{\Omega}}x^{p}\_{cu}\right)^{\frac{1}{p}}\right]\_{c=1,\cdots,C} $$
In this equation, "p" is a parameter that controls the exponent of the mean, "x" is the tensor that represents the input feature map, "c" is the channel index, and "u" is the position index in the receptive field. The variable "Omega" represents the set of positions in the receptive field that are pooled together to compute the output.
When the exponent "p" is set to 1, the generalized mean pooling function reduces to the average pooling operation, which computes the arithmetic mean of the values in each channel over the set of pooled positions. In contrast, increasing the value of "p" above 1 increases the contrast of the pooled feature map and focuses on the salient features of the image.
Setting the exponent to infinity leads to the spatial max-pooling operation, which computes the maximum value in each channel over the set of pooled positions.
Applications of Generalized Mean Pooling
Generalized mean pooling can be applied in a variety of deep learning tasks, including image classification, object detection, and semantic segmentation. In these tasks, the pooling operation is typically used to reduce the spatial resolution of the feature maps before feeding them into subsequent layers of the network. By reducing the resolution, the amount of computations required by the network can be reduced, making it more efficient.
One advantage of using generalized mean pooling over other pooling operations, such as average pooling or max-pooling, is that it preserves more information about the spatial structure of the input feature maps. This can be particularly useful in tasks such as object detection or semantic segmentation, where the spatial location of objects or regions of interest is important.
Advantages and Disadvantages of Generalized Mean Pooling
One major advantage of generalized mean pooling is that it allows for more control over the pooling operation than other pooling methods. By adjusting the value of the exponent "p", it is possible to emphasize different aspects of the input feature maps, such as fine-grained details or high-level features.
Another advantage of generalized mean pooling is that it preserves more information about the spatial structure of the input feature maps. This can be particularly useful in tasks such as object detection or semantic segmentation, where the spatial location of objects or regions of interest is important.
One disadvantage of generalized mean pooling is that it can be computationally expensive compared to other pooling methods. This is because it involves computing a power and a root operation for each channel in the feature map.
Generalized Mean Pooling (GeM) is a mathematical operation used in deep learning to compute the generalized mean of each channel in a tensor. It is a generalization of the average pooling, which is commonly used in classification networks, and of spatial max-pooling layer. By applying GeM, it is possible to increase the contrast of the pooled feature map and focus on the salient features of the image. Generalized mean pooling can be applied in a variety of deep learning tasks, including image classification, object detection, and semantic segmentation. However, it can be computationally expensive compared to other pooling methods.
