Semi-supervised Continual Learning with Pseudo-labels Based on the Output of a Deep Neural Network

Abstract

We address semi-supervised continual learning for learning with labeled data, which is assumed to be abundant in existing continual learning research, in situations where it is not sufficiently available in the real world. We propose a semi-supervised continual learning method that uses soft outputs of a deep neural network as pseudo-labels for an image classification task, and define a semi-supervised continual learning scenario. In the experiments, the proposed and compared methods are used in several continual learning scenarios and evaluated based on the step-by-step accuracy and final accuracy as the new class increases, as well as the average accuracy over all steps.