Improved Training for Self Training by Confidence Assessments

Abstract

It is well known that for some tasks, labeled data sets may be hard to gather. Self-training, or pseudo-labeling, tackles the problem of having insufficient training data. In the self-training scheme, the classifier is first trained on a limited, labeled dataset, and after that, it is trained on an additional, unlabeled dataset, using its own predictions as labels, provided those predictions are made with high enough confidence. Using credible interval based on MC-dropout as a confidence measure, the proposed method is able to gain substantially better results comparing to several other pseudo-labeling methods and outperforms the former state-of-the-art pseudo-labeling technique by 7 % on the MNIST data-set. In addition to learning from large and static unlabeled datasets, the suggested approach may be more suitable than others as an online learning method where the classifier keeps getting new unlabeled data. The approach may be also applicable in the recent method of pseudo-gradients for training long sequential neural networks.

The Automated Inspection of Opaque Liquid Vaccines

“…The learned rules are used to assign labels to unlabelled samples, allowing a fresh classifier to be trained using a larger dataset. This bootstrapping approach is useful for tasks where gathering large amounts of labelled data is infeasible due to the cost associated with hand-labelling samples [1]. However, automatic labelling requires considerations regarding reducing the impact of noisy labels resulting from mis-classification [29].…”