Masked Supervised Learning

Masked supervised learning (MSL) is a machine learning technique that improves model performance by training on partially obscured data, forcing the model to learn robust and context-aware representations. Current research focuses on applying MSL to various tasks, including image and video processing, using architectures like Vision Transformers (ViTs) and incorporating techniques like masked patch prediction and masked motion prediction. This approach is particularly valuable in scenarios with limited labeled data, enhancing performance in applications such as medical image analysis, video compression, and activity recognition, ultimately leading to more efficient and effective models.