Conceptos Básicos
DnD proposes a novel method to describe hidden neurons in vision networks using language models, outperforming prior work.
Resumen
The paper introduces Describe-and-Dissect (DnD) as a method to interpret the roles of hidden neurons in vision networks. DnD leverages multimodal deep learning advancements to generate natural language descriptions without labeled training data. The method is training-free and provides high-quality neuron descriptions compared to previous works. The content is structured into sections covering Introduction, Related Work, Methods, Experiments, and Conclusions. Extensive qualitative and quantitative analysis supports the effectiveness of DnD in interpreting neuron functionalities.
Introduction:
Recent advancements in Deep Neural Networks have revolutionized various domains but lack interpretability.
DnD aims to provide a method for understanding the functionality of individual neurons in vision networks.
Related Work:
Previous methods like Network Dissection and CLIP-Dissect have limitations in describing concepts detected by neurons.
DnD overcomes these limitations by generating generative descriptions for neurons without requiring labeled concept data.
Methods:
DnD pipeline consists of three steps: Probing Set Augmentation, Candidate Concept Generation, and Best Concept Selection.
Attention Cropping is used to highlight salient regions on activation maps for better interpretation.
GPT model is utilized for summarizing similarities between image captions generated for highly activating images.
Experiments:
Qualitative evaluation shows that DnD captures higher-level concepts more coherently than baseline methods.
Quantitative evaluation demonstrates superior results of DnD compared to MILAN on ResNet-50's final layer.
Crowdsourced experiments confirm that DnD outperforms other methods consistently across different models and layers.
Conclusions:
DnD presents a novel approach to interpreting neuron functionalities in vision networks effectively.
Estadísticas
提案された手法は、以前の作業を上回る高品質なニューロンの説明を提供します。