/SI-SmI-Implementation-and-Application

SI (Seperation Index) and SmI (Smoothness Index) are some metrics proposed by Prof. Ahmad Kalhor at university of Tehran. These metrics can be used in a wide range of applications such as "compressing neural networks" and "extrapolating relationship in data without training a model" .

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SI-SmI-Implementation-and-Application

SI (Seperation Index) and SmI (Smoothness Index) are some metrics proposed by Prof. Ahmad Kalhor at university of Tehran.

These metrics can be used in a wide range of applications such as "compressing neural networks" and "extrapolating relations in data without training a model" .

This repository contains implementation and applications of some variants of these metrics.

For more technical details and results read the following paper by Prof. Kalhor and A. Karimi :
Learning Enhancement of CNNs via Separation Index Maximizing at the First Convolutional Layer