完整的LoRA:有效地提高预训练的视觉转换器的稳定性
概括
研究人员开发了FullLoRA,以提高视觉变压器 (ViT) 模型对抗对方攻击的稳定性. 这种方法使用了一些额外的参数,以实现高效的对抗微调,提高安全性,而无需大量的培训成本.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习安全 深度学习安全
背景情况:
- 视觉变压器 (ViT) 模型被广泛使用,但可能缺乏对抗攻击的稳定性.
- 在培训中优先考虑绩效可能会损害模型安全,引发重大担忧.
研究的目的:
- 调查参数有效的对抗微调,以提高ViT的稳定性.
- 开发一种方法,使用最小的额外参数快速有效地提高对抗性弹性.
主要方法:
- 引入了LNLoRA模块,具有可学习层规范化,先于LoRA模块,以解决参数大小差异.
- 提出了FullLoRA框架,将LNLoRA模块集成到ViT组件中,同时结预训练模型.
- 在FullLoRA框架内实施对抗微调,以提高参数有效的稳定性.
主要成果:
- 与标准训练相比,FullLoRA表现出优越的对抗性强度.
- 实现了与完全微调相当的稳定性,仅使用大约5%的可学习参数.
- 有效地缓解了与增加的存储和对对抗微调的培训时间相关的担忧.
结论:
- FullLoRA框架提供了一个参数有效的解决方案,用于增强ViT的对抗性稳定性.
- 这种方法可以显著提高模型安全性,而无需过度的计算开销.
- FullLoRA介绍了一种用于在安全敏感应用中部署强大的ViT模型的实用方法.
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