Patch Masking

Patch masking, a technique involving selectively obscuring parts of input data, is used in various machine learning contexts to improve model robustness, efficiency, and interpretability. Current research focuses on developing sophisticated masking strategies beyond simple random masking, exploring data-independent methods, adaptive masking based on input characteristics, and task-specific masking learned via optimization. These advancements are impacting diverse fields, including image classification, medical image analysis, and natural language processing, by enhancing model performance, reducing computational costs, and providing insights into model behavior. The development of more effective and efficient masking techniques continues to be a significant area of investigation.