We address the functional role of 'feature inhibition' in vision models; that
is, what are the mechanisms by which a neural network ensures images do not
express a given feature? We observe that standard interpretability tools in the
literature are not immediately suited to the inhibitory case, given the
asymmetry introduced by the ReLU activation function. Given this, we propose
inhibition be understood through a study of 'maximally tense images' (MTIs),
i.e. those images that excite and inhibit a given feature simultaneously. We
show how MTIs can be studied with two novel visualization techniques; +/-
attribution inversions, which split single images into excitatory and
inhibitory components, and the attribution atlas, which provides a global
visualization of the various ways images can excite/inhibit a feature. Finally,
we explore the difficulties introduced by superposition, as such interfering
features induce the same attribution motif as MTIs.

本论文研究了视觉模型中 ' 特征抑制 ' 的功能作用，即神经网络如何确保图像不表达给定的特征，提出通过研究 ' 最紧张的图像 '（MTIs）来理解抑制作用，介绍了两种新的可视化技术和大量探索性研究。