Random Masking

Random masking, a technique where portions of input data are randomly obscured, is used in various machine learning contexts to improve model robustness, efficiency, and performance. Current research focuses on optimizing masking strategies beyond simple random selection, exploring methods like Gaussian, motion-guided, and attention-based masking within architectures such as masked autoencoders, vision transformers, and diffusion models. These advancements aim to enhance the learning of richer representations, improve generalization across diverse datasets, and reduce computational costs in applications ranging from image processing and natural language processing to federated learning and anomaly detection.