Booster-Driven Consistency Training Framework for Semi-Supervised Learning

Abstract

The paper proposes an inductive semi-supervised learning method, called Booster-Driven Consistency Training (BDCT). In our work, we extend consistency training by designing a “booster” module for aggregating multi-view information, preventing the networks from collapsing into each other, and constructing better targets in parallel with the existing consistency training method. By booster, we mean that the one network is treated as the booster to help the training of the other two co-trained and superior networks, “spacecraft”. Furthermore, BDCT integrates smoothness enforcing into the designed fast feedback loop to ensure the effectiveness and the robustness of training. Meanwhile, adversarial examples are exploited to maintain the diversity among the networks that are learned to be smooth on the low dimensional manifold. BDCT demonstrates satisfactory performance in comparison with state-of-the-art semi-supervised deep learning methods and extensive experiments validate the effectiveness of the “booster” module.