从独立补丁到协调注意力：在视觉变换器中控制信息流
From independent patches to coordinated attention: Controlling information flow in vision transformers
AI总结：
该研究通过引入信息瓶颈控制视觉变换器中信息流，探索从局部补丁处理到全局注意力的可控光谱，并揭示了全局视觉表示的形成机制。
摘要：
我们使视觉变换器中通过注意力传递的信息成为显式且可测量的量。通过在所有通过注意力传递到残差流的写入操作上插入变分信息瓶颈——而无需其他架构改动——我们训练出具有显式信息成本的模型，从而获得从独立补丁处理到完全表达的全局注意力的可控光谱。在ImageNet-100上，我们刻画了在该光谱中分类行为和信息路由如何演变，并通过分析首个传输信息的注意力头，提供了关于全局视觉表示如何从局部补丁处理中产生的初步见解。通过将学习偏向于具有受限内部通信的解决方案，我们的方法产生出更易于机制分析且更易于控制的模型。
英文摘要：
We make the information transmitted by attention an explicit, measurable quantity in vision transformers. By inserting variational information bottlenecks on all attention-mediated writes to the residual stream -- without other architectural changes -- we train models with an explicit information cost and obtain a controllable spectrum from independent patch processing to fully expressive global attention. On ImageNet-100, we characterize how classification behavior and information routing evolve across this spectrum, and provide initial insights into how global visual representations emerge from local patch processing by analyzing the first attention heads that transmit information. By biasing learning toward solutions with constrained internal communication, our approach yields models that are more tractable for mechanistic analysis and more amenable to control.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
