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Deep neural networks have exhibited remarkable performance across a widerange of real-world tasks. However, comprehending the underlying reasons fortheir effectiveness remains a challenging problem. Interpreting deep neuralnetworks through examining neurons offers distinct advantages when it comes toexploring the inner workings of neural networks. Previous research hasindicated that specific neurons within deep vision networks possess semanticmeaning and play pivotal roles in model performance. Nonetheless, the currentmethods for generating neuron semantics heavily rely on human intervention,which hampers their scalability and applicability. To address this limitation,this paper proposes a novel post-hoc framework for generating semanticexplanations of neurons with large foundation models, without requiring humanintervention or prior knowledge. Our framework is designed to be compatiblewith various model architectures and datasets, facilitating automated andscalable neuron interpretation. Experiments are conducted with both qualitativeand quantitative analysis to verify the effectiveness of our proposed approach.

Automated Natural Language Explanation of Deep Visual Neurons with Large Models