Semisupervised Learning for Noise Suppression Using Deep Reinforcement Learning of Contrastive Features

In this letter, we present DeDRLSSL: a generic semisupervised noise suppression framework. The proposed model is based on a reinforcement learning system for learning contrastive features to refine the features utilized in consistency matching for semisupervised learning (SSL). The proposed method outperforms the state-of-the-art supervised models in terms of error compensation for Inertial Measurement Unit data from various evaluation metrics and improves the baselines for yaw estimation on average by 38% and 28% across the benchmarks for 30% and 50% of labeled data, respectively. Our approach can be adapted to any SSL approach to compensate for the problem of label scarcity.