Erase Based Masking

Erase-based masking, a technique involving selectively removing or obscuring parts of input data, is used in various machine learning domains to improve model robustness, efficiency, and privacy. Current research focuses on optimizing masking strategies (e.g., frequency-based, Gaussian, or semantic-guided masking) within different architectures, including convolutional neural networks (CNNs), vision transformers (ViTs), and graph neural networks (GNNs), to enhance self-supervised learning, improve downstream task performance, and protect sensitive information. This approach has shown promise in diverse applications such as image and speech processing, natural language processing, and medical image analysis, demonstrating its significance for advancing both theoretical understanding and practical applications of machine learning.