An Attention Mechanism is a neural network component that dynamically focuses on specific parts of the input data when making predictions, allowing the model to prioritize relevant information while disregarding less important details. Initially developed for machine translation, attention mechanisms have become foundational in various AI applications, such as natural language processing and computer vision, by enhancing the model’s ability to understand context and dependencies in complex data, leading to improved performance and accuracy.
