Attention Masking

Attention masking is a technique used in various machine learning models to selectively focus on or ignore specific parts of input data, improving model performance and robustness. Current research focuses on applying attention masking within transformer architectures for tasks like multimodal document classification, machine translation, and image generation, often incorporating it into contrastive learning frameworks or as a component of novel model designs. This technique is proving valuable in addressing challenges such as out-of-distribution detection, rare word translation, and improving the alignment between visual and textual information in multimodal models, leading to advancements in diverse applications including medical image analysis and text-to-image synthesis.