Enhancing Siamese Neural Networks for Multi-class Classification: An Immuno-inspired approach

Siamese Neural Networks (SNN) are known to perform well in resource-constrained scenarios where computation and data availability are limited. They utilise the similarity space of a given dataset to extract distinguishing features between dissimilar data samples. Such features have also been utilised for classification tasks. Though several works on enhancing the accuracies and inference times using such similarity spaces have been reported, there is still scope for investigations that can yield more efficient strategies. The Biological Immune System (BIS) is known for employing such a transformation to recognise and contain antigenic attacks. Concepts from a BIS can thus, aid in boosting the classification performance of SNNs. This paper summarizes such an attempt made in our work "Immuno-Inspired Augmentation of Siamese Neural Network for Multi-class Classification" [8] presented at IVCNZ 2022, first published in Lecture Notes in Computer Science, 2023, vol 13836, pages 486--500 by Springer. A novel SNN-based multi-class classification method augmented with an immuno-inspired approach that allows an SNN to plug class-specific characteristics into its architecture is presented herein. The empirical analyses and results conducted on three benchmark datasets, clearly indicate that this method delivers higher accuracies and lower inference times when compared to recent SNN-based multi-class classification techniques.