Maximally Compact and Separated Features with Regular Polytope Networks

TL;DR

This paper proposes a method to extract highly discriminative features from CNNs by fixing the classifier parameters, leading to maximally separated and compact features with fewer parameters and no auxiliary losses.

Contribution

It introduces a fixed classifier approach that unifies and generalizes discriminative feature learning and fixed classifiers, improving feature quality and network efficiency.

Findings

Features with maximum inter-class separability and intra-class compactness

Reduced network parameters and simplified training process

Potential for improved classification performance

Abstract

Convolutional Neural Networks (CNNs) trained with the Softmax loss are widely used classification models for several vision tasks. Typically, a learnable transformation (i.e. the classifier) is placed at the end of such models returning class scores that are further normalized into probabilities by Softmax. This learnable transformation has a fundamental role in determining the network internal feature representation. In this work we show how to extract from CNNs features with the properties of \emph{maximum} inter-class separability and \emph{maximum} intra-class compactness by setting the parameters of the classifier transformation as not trainable (i.e. fixed). We obtain features similar to what can be obtained with the well-known ``Center Loss'' \cite{wen2016discriminative} and other similar approaches but with several practical advantages including maximal exploitation of the available feature space representation, reduction in the number of network parameters, no need to use other auxiliary losses besides the Softmax. Our approach unifies and generalizes into a common approach two apparently different classes of methods regarding: discriminative features, pioneered by the Center Loss \cite{wen2016discriminative} and fixed classifiers, firstly evaluated in \cite{hoffer2018fix}. Preliminary qualitative experimental results provide some insight on the potentialities of our combined strategy.