Describe-and-Dissect: Interpreting Neurons in Vision Networks with Language Models
Primary Area: visualization or interpretation of learned representations
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Keywords: Interpretability, Explainability, Deep Learning
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TL;DR: We propose an automated method to generate comprehensive descriptions of representations of neurons in the deep neural networks
Abstract: In this paper, we propose Describe-and-Dissect, a novel method to describe the roles of hidden neurons in vision networks. Describe-and-Dissect 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, Describe-and-Dissect is training-free, meaning we don't train any new models and can easily leverage more capable general purpose models in the future. We show on a large scale user study that our method outperforms the state-of-the-art baseline methods including CLIP-Dissect, MILAN, and Network Dissection. 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.
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Submission Number: 6902
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