Hard Attention

Hard Attention is a mechanism in deep learning used for selectively focusing on specific parts of the input data. By explicitly selecting one or more elements from the input sequence at each time step, Hard Attention can enhance the efficiency and performance of the model, reducing the consumption of computational resources. This mechanism has significant application value in fields such as natural language processing and computer vision, especially in tasks that require extracting key information from large amounts of data.