Method of training a neural network for predictive tasks and associated apparatus and system

Abstract

Methods, apparatuses and systems of training neural networks for predictive tasks are disclosed. The method includes training a neural network within a closed-loop framework, where the focus is on predicting target tasks for an object. The method identifies situations and inputs (i.e., augmentations) that lead to higher errors in the neural network's predictions by calculating a loss value between predicted target tasks and known target tasks during a baseline training period. An optimizer uses the loss value to predict correlations between augmentations and loss values and identifies difficult augmentations. The difficult augmentations are then intentionally emphasized during subsequent training periods, to increase the likelihood that the neural network is trained on difficult augmentations to enhance the neural network's capacity to adapt and learn from error-prone conditions.