Learning Enhancement of CNNs via Separation Index Maximizing at the First Convolutional Layer. (arXiv:2201.05217v1 [cs.LG])

In this paper, a straightforward enhancement learning algorithm based on
Separation Index (SI) concept is proposed for Convolutional Neural Networks
(CNNs). At first, the SI as a supervised complexity measure is explained its
usage in better learning of CNNs for classification problems illustrate. Then,
a learning strategy proposes through which the first layer of a CNN is
optimized by maximizing the SI, and the further layers are trained through the
backpropagation algorithm to learn further layers. In order to maximize …

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