# On Intra-Class Variance for Deep Learning of Classifiers

## TL;DR

This paper introduces a new deep learning technique for image classifiers that improves feature separation and class probability stability by incorporating intra-class variance and a Hadamard layer, enhancing training efficiency and adaptability.

## Contribution

The paper proposes a novel neural network layer and intra-class variance extension that improve feature separation and training efficiency in deep image classifiers.

## Key findings

- Enhanced feature separation measured by Euclidean proximity.
- No deterioration in classification accuracy with class membership probability.
- Faster convergence to comparable accuracy in fewer epochs.

## Abstract

A novel technique for deep learning of image classifiers is presented. The learned CNN models offer better separation of deep features (also known as embedded vectors) measured by Euclidean proximity and also no deterioration of the classification results by class membership probability. The latter feature can be used for enhancing image classifiers having the classes at the model's exploiting stage different from from classes during the training stage. While the Shannon information of SoftMax probability for target class is extended for mini-batch by the intra-class variance, the trained network itself is extended by the Hadamard layer with the parameters representing the class centers. Contrary to the existing solutions, this extra neural layer enables interfacing of the training algorithm to the standard stochastic gradient optimizers, e.g. AdaM algorithm. Moreover, this approach makes the computed centroids immediately adapting to the updating embedded vectors and finally getting the comparable accuracy in less epochs.