Optimizing Feature Map Pooling
Uncover the effectiveness of tailored maximum likelihood estimators for pooling in convolutional networks.
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A feature map follows a distribution. The distribution differs with samples. For example, an object with sharp edges at the center of an image will have a different feature map distribution compared to an object with smudgy edges or located at a corner.
The distribution’s maximum likelihood estimator (MLE) makes the most efficient pooling statistic. Here are a few distributions that feature maps typically follow and their MLEs.
Uniform distribution
A uniform distribution belongs to the symmetric location probability distribution family. A uniform distribution describes a process where the random variable has an arbitrary outcome in a boundary denoted as with the same probability. Its pdf is
Different shapes of the uniform distribution are shown in the following illustration as examples. Feature maps can follow a uniform distribution under some circumstances, such as if the object of interest is scattered in an image.
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