In this paper, we propose Describe-and-Dissect (DnD), a novel method to
describe the roles of hidden neurons in vision networks. DnD utilizes recent
advancements in multimodal deep learning to produce complex natural language
descriptions, without the need for labeled training data or a predefined set of
concepts to choose from. Additionally, DnD is training-free, meaning we don't
train any new models and can easily leverage more capable general purpose
models in the future. We have conducted extensive qualitative and quantitative
analysis to show that DnD outperforms prior work by providing higher quality
neuron descriptions. Specifically, our method on average provides the highest
quality labels and is more than 2 times as likely to be selected as the best
explanation for a neuron than the best baseline.

本文提出了一种描述和解析（DnD）的新方法，利用多模态深度学习的最新进展来产生复杂的自然语言描述，无需带标签的训练数据或预定义的概念选择集。通过广泛的定性和定量分析，我们展示了 DnD 在提供更高质量神经元描述方面的优越性，并且与最佳基线相比，我们的方法平均提供了最高质量的标签，并且更有可能被选为神经元的最佳解释。