Noisy Student Training

Noisy student training is a semi-supervised learning technique that improves model performance by training a "student" model on noisy versions of data generated by a "teacher" model. Current research focuses on optimizing this approach for various applications, including speech recognition (ASR), natural language processing (NLP), and music emotion recognition, often incorporating techniques like CycleGANs, data filtering strategies, and layer-specific noise injection to enhance robustness and efficiency. This method's significance lies in its ability to leverage unlabeled data, reducing reliance on expensive human annotation while achieving state-of-the-art results in diverse domains, particularly where labeled data is scarce.