Automated Natural Language Explanation of Deep Visual Neurons with Large
  Models
Chenxu Zhao Wei Qian Yucheng Shi Mengdi Huai Ninghao Liu
Deep neural networks have exhibited remarkable performance across a wide
range of real-world tasks. However, comprehending the underlying reasons for
their effectiveness remains a challenging problem. Interpreting deep neural
networks through examining neurons offers distinct advantages when it comes to
exploring the inner workings of neural networks. Previous research has
indicated that specific neurons within deep vision networks possess semantic
meaning and play pivotal roles in model performance. Nonetheless, the current
methods 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 semantic
explanations of neurons with large foundation models, without requiring human
intervention or prior knowledge. Our framework is designed to be compatible
with various model architectures and datasets, facilitating automated and
scalable neuron interpretation. Experiments are conducted with both qualitative
and quantitative analysis to verify the effectiveness of our proposed approach.