Masked Unsupervised Self Training

Masked unsupervised self-training (MUST) focuses on improving the performance of pre-trained models on downstream tasks using only unlabeled data, addressing the limitations of supervised learning's reliance on expensive labeled datasets. Current research explores various masking strategies and model architectures, including transformers and masked autoencoders, to leverage both global and local feature learning from unlabeled data for tasks like image classification, question answering, and remaining useful life prediction. This approach offers significant potential for enhancing model efficiency and adaptability across diverse domains, particularly in scenarios with limited labeled data, thereby broadening the applicability of deep learning techniques.