Masking Strategy

Masking strategies in machine learning involve selectively obscuring parts of input data during training to improve model performance and robustness. Current research focuses on optimizing masking techniques for various model architectures, including Vision Transformers and Convolutional Neural Networks, often employing self-supervised learning paradigms like masked autoencoders and contrastive learning. These advancements aim to enhance feature learning, improve efficiency, mitigate biases, and address challenges such as catastrophic forgetting and adversarial attacks, ultimately leading to more accurate and reliable models across diverse applications like image classification, segmentation, and natural language processing.