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

Abstract

In this work, we present DeDRLSSL a generic semi-supervised noise suppression framework. The proposed model is based on a reinforcement learning (RL) system for learning contrastive features to refine the features utilized in consistency matching for semi-supervised learning (SSL). The proposed method outperforms the state-of-the-art supervised models in terms of error compensation for Inertial Measurement Unit (IMU) 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.