We developed a tool for visualizing and analyzing large pre-trained vision
models by mapping them onto the brain, thus exposing their hidden inside. Our
innovation arises from a surprising usage of brain encoding: predicting brain
fMRI measurements in response to images. We report two findings. First,
explicit mapping between the brain and deep-network features across dimensions
of space, layers, scales, and channels is crucial. This mapping method,
FactorTopy, is plug-and-play for any deep-network; with it, one can paint a
picture of the network onto the brain (literally!). Second, our visualization
shows how different training methods matter: they lead to remarkable
differences in hierarchical organization and scaling behavior, growing with
more data or network capacity. It also provides insight into finetuning: how
pre-trained models change when adapting to small datasets. Our method is
practical: only 3K images are enough to learn a network-to-brain mapping.

我们开发了一种工具，通过将大型预训练视觉模型映射到大脑上，从而揭示其隐藏在内部的信息。我们的创新提出了一种令人惊讶的使用大脑编码的方法：预测大脑在图像刺激下的功能磁共振成像测量。我们报告了两个发现：首先，对于空间、层级、尺度和通道的大脑和深度网络特征之间的显式映射至关重要。这种映射方法，FactorTopy，可应用于任何深度网络；通过它，我们可以将网络映射到大脑上（真实可视化）。其次，我们的可视化结果显示了不同的训练方法的重要性：它们导致了层次结构和缩放行为上显著的差异，随着更多的数据或网络容量增加而增长。它还提供了微调的见解：在适应小数据集时，预训练模型如何改变。我们的方法很实用：只需 3000 个图像就足以学习网络到大脑的映射。