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.

翻译：本文提出“描述与剖析”（Describe-and-Dissect, DnD）方法，用于揭示视觉网络中隐藏神经元的作用。DnD利用多模态深度学习的最新进展生成复杂的自然语言描述，无需标注训练数据或预设概念集合。此外，DnD无需训练过程，即不训练任何新模型，并可轻松兼容未来更强大的通用模型。我们通过大量定性与定量分析证明，DnD生成的神经元描述质量优于现有方法。具体而言，本方法平均提供了最高质量的标签，且被选为最佳神经元解释的概率是最优基准方法的两倍以上。