Efficient Transformers: A Survey
This survey helps researchers understand efficiency improvements in Transformer architectures across domains like language, vision, and reinforcement learning.
This paper provides a comprehensive survey of recent Transformer variants that improve computational and memory efficiency, characterizing a large selection of 'X-former' models across multiple domains to help researchers navigate this rapidly evolving field.
Transformer model architectures have garnered immense interest lately due to their effectiveness across a range of domains like language, vision and reinforcement learning. In the field of natural language processing for example, Transformers have become an indispensable staple in the modern deep learning stack. Recently, a dizzying number of "X-former" models have been proposed - Reformer, Linformer, Performer, Longformer, to name a few - which improve upon the original Transformer architecture, many of which make improvements around computational and memory efficiency. With the aim of helping the avid researcher navigate this flurry, this paper characterizes a large and thoughtful selection of recent efficiency-flavored "X-former" models, providing an organized and comprehensive overview of existing work and models across multiple domains.