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基于生命回归的补丁减肥视力变换器的补丁减肥

Jiawei Chen1, Lin Chen1, Jiang Yang2

  • 1State Key Laboratory of Blockchain and Security, Zhejiang University , Hangzhou, 310027, Zhejiang, China; Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security , Hangzhou, 310027, Zhejiang, China.

Neural networks : the official journal of the International Neural Network Society
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此摘要是机器生成的。

这项研究引入了一种用于视觉转换器的新补丁减肥方法,在一步内确定补丁寿命,以加快推断. 这种方法可以减少计算和训练时间,同时保持性能.

关键词:
加速加速的加速模型优化模型优化视觉变压器 视觉变压器

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科学领域:

  • 计算机视觉 计算机视觉
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 人工智能的人工智能

背景情况:

  • 视觉转换器 (ViTs) 擅长通过自我注意力捕捉图像依赖性.
  • 在ViT中,高推断成本阻碍了实际应用.
  • 目前的补丁减肥方法增加了多层计算.

研究的目的:

  • 为视觉变压器提出一种高效的补丁减肥方法.
  • 通过减少冗余计算来加速ViT推断.
  • 为了保持具有竞争力的性能,减少计算开销.

主要方法:

  • 引入了一个生命回归模块来确定补丁寿命.
  • 在推断过程中,根据其预先确定的寿命来丢弃补丁.
  • 避免了跨多层的额外计算和参数.

主要成果:

  • 在没有显著的性能下降的情况下实现了增强的推断速度.
  • 与现有方法相比,减少了培训时代的数量.
  • 证明了一种更有效的补丁减肥方法.

结论:

  • 拟议的生命回归模块为ViT推理加速提供了有效的解决方案.
  • 这种方法提供了计算效率和模型准确性之间的权衡.
  • 它为优化基于变压器的计算机视觉模型提供了一个有希望的方向.