Fault Tolerance in Triplet Network Training: Analysis, Evaluation and Protection Methods

Abstract

This paper investigates the tolerance of Triplet Networks (TNs) with a focus on faults in the training process. For compatibility with the existing literature. So-called stuck-at faults of a functional nature are considered for the operation of the neurons and activation function. While TNs are shown to be generally robust against such faults in the anchor and positive subnetworks, the presented analysis reveals a significant vulnerability in the negative subnetwork, in which stuck-at faults can lead to false convergence and training failures. An in-depth treatment is provided to show the incorrect convergence of training in the presence of stuck-at faults, highlighting the behavior of the network with faulty neurons. Extensive simulations are presented to evaluate the impact of these faults and propose two innovative fault-tolerant methods: the regularization of the anchor outputs and the modified margin. Simulation shows that false convergence can be very efficiently avoided by utilizing the proposed techniques, and thus the overall accuracy loss of the TN is negligible. These findings contribute to the understanding of fault tolerance in emerging neural networks such as TNs and offer practical solutions for enhancing their robustness against faults.