Method for stabilizing neural pathways in early-phase artificial neural network training

Abstract

A method and system for training an artificial neural network (ANN) is disclosed, specifically addressing the technical problem of training instability caused by randomly initialized weights. The invention introduces a novel, multi-phase training protocol executed by a processor. During an initial stabilization phase, the processor modifies the backpropagation of a weighted loss function by deprioritizing training on data from classes for which the ANN has a confidence level below a predetermined low threshold (e.g., less than or equal to 25 percent). This initial, focused training prevents erratic gradient updates to the weights of the network's layers, thereby forming stable and rigorous foundational neural pathways. Subsequent to the stabilization phase, a dynamic training phase commences where the network trains on the full dataset, with the training focus adjusted in real-time to reinforce the stable pathways. This protocol results in faster training convergence, a more robust final model, and improved accuracy, particularly for complex datasets, representing a significant technical improvement to the functioning of machine learning systems.

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