A General Survey on Attention Mechanisms in Deep Learning

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...

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Veröffentlicht in:IEEE transactions on knowledge and data engineering Jg. 35; H. 4; S. 3279 - 3298
Hauptverfasser: Brauwers, Gianni, Frasincar, Flavius
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Attention models
Biological system modeling
Computational modeling
Data models
Deep learning
Feature extraction
introductory and survey
neural nets
supervised learning
Task analysis
Taxonomy
ISSN:1041-4347, 1558-2191
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2021.3126456