法国:用于自适应性ViT推断的联合令牌优化和结构道剪裁
IEEE transactions on pattern analysis and machine intelligence
|September 2, 2025
概括
通过共同优化每个样本的频道和令牌,PRANCE加速视觉转换器 (ViT). 这种框架减少了计算复杂性和模型大小,而不会牺牲准确性,从而实现了高效的ViT部署.
科学领域:
- 计算机视觉
- 人工智能
- 机器学习
背景情况:
- 视觉转换器 (ViT) 面临着部署挑战,因为模型大小和代码数的二次复杂性.
- 对于加速ViT的现有方法,如修剪和令牌减少,通常使用固定的比率,并忽视联合优化,导致准确性损失.
研究的目的:
- 引入PRANCE,这是一个新的框架,用于共同优化每个样本的激活道和代币,以加快ViT推断.
- 解决动态通道计算和联合优化的巨大决策空间的挑战.
主要方法:
- 在多头自我注意 (MHSA) 和多层感知器 (MLP) 层中开发了一个具有重量共享的元网络.
- 使用近距离策略优化 (PPO) 通过轻量级选择器有效管理组合优化问题.
- 引入了一个"结果即可"的训练机制,将ViT推断作为马尔科夫决策过程,以减少行动空间和奖励延迟.
主要成果:
- 在FLOP (浮点操作) 中实现了约50%的减少.
- 只有大约10%的输入令牌被保留.
- 保持无损的Top-1准确性,显示了显著的效率增长.
结论:
- 通过同时优化架构和数据,PRANCE提供了一种统一的方法来加速ViT.
- 该框架有效地解决了压缩和准确性之间的权衡,使ViT能够有效地部署.
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