A General Survey on Attention Mechanisms in Deep Learning
It organizes existing knowledge for researchers and practitioners, but is incremental as it reviews prior work without new results.
This survey provides an overview of important attention mechanisms in deep learning, explaining them through a unified framework, taxonomy, and evaluation measures.
Attention is an important mechanism that can be employed for a variety of deep learning models across many different domains and tasks. This survey provides an overview of the most important attention mechanisms proposed in the literature. The various attention mechanisms are explained by means of a framework consisting of a general attention model, uniform notation, and a comprehensive taxonomy of attention mechanisms. Furthermore, the various measures for evaluating attention models are reviewed, and methods to characterize the structure of attention models based on the proposed framework are discussed. Last, future work in the field of attention models is considered.