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简介: CAIT:三赢压缩实现高精度,快速推理,并为ViTs提供有利的可转移性
IEEE transactions on pattern analysis and machine intelligence
|October 1, 2025
概括
视觉变压器 (ViT) 使用一种新的联合方法,CAIT,平衡精度和速度进行压缩. 这种方法有效地合并了代币和修剪道,提高了下游任务的性能.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 视觉变压器 (ViTs) 实现了最先进的性能,但具有高的计算需求.
- 对于ViT而言,现有的压缩方法在性能和速度之间提供了低于最佳的权衡.
- 当前的方法难以转移到需要空间图像结构的任务中.
研究的目的:
- 为ViT开发一种联合压缩方法,以提高准确性,推断速度和可转移性.
- 为了解决现有的代币和道修剪技术的局限性.
- 在资源有限的设备上实现ViT的高效部署.
主要方法:
- 引入了一个不对称的令牌合并 (ATME) 策略,以整合邻近的令牌,同时保留空间信息.
- 开发了一种一致的动态频道修剪 (CDCP) 策略,以在多头自我注意模块中均修剪频道.
- 实施了一种联合压缩方法,将ATME和CDCP用于视觉变压器.
主要成果:
- 拟议的CAIT方法在模型准确性和推断速度之间实现了卓越的平衡.
- 证明了ViTs.有效压缩冗余的令牌和道信息.
- 在多个基准数据集上,在各种ViT中实现了最先进的性能.
结论:
- 凯特为视觉变压器提供了一个高效的联合压缩策略.
- 该方法显著提高了推断速度和准确性,而不会影响空间结构的保存.
- 凯特增强了压缩ViT对下游视觉任务的可转移性.
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