Token Masking

Token masking, a technique used in training language and vision models, involves strategically obscuring parts of the input data to encourage the model to learn more robust and meaningful representations. Current research focuses on improving masking strategies beyond random selection, exploring methods like attention-guided masking and scheduled masking that adapt to the training process or prioritize informative tokens. These advancements aim to enhance model performance on downstream tasks and improve the explainability and faithfulness of model predictions, impacting fields like natural language processing and computer vision.