E-TCAV: Formalizing Penultimate Proxies for Efficient Concept Based Interpretability
Researchers have introduced E-TCAV, a new framework designed to enhance the efficiency of Testing with Concept Activation Vectors (TCAV), a popular method for interpreting neural networks. While TCAV effectively aligns internal network representations with human-understandable concepts, it traditionally suffers from high computational costs, statistical instability, and inconsistent scores across different layers. The E-TCAV framework addresses these limitations by utilizing the penultimate layer of neural networks as a fast proxy for earlier layers. Extensive evaluations across four architectures and five datasets in computer vision and natural language processing reveal that final block layers strongly agree with the penultimate layer regarding TCAV scores. Furthermore, the study attributes common score variance to the choice of latent classifiers. By leveraging inter-layer agreement and directional sensitivity degeneracy, E-TCAV achieves linearly scaling speed-ups relative to network size and sample count. This advancement facilitates more efficient model debugging and enables real-time concept-guided training, marking a significant step forward in making AI interpretability methods more practical and scalable for complex deep learning applications.
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E-TCAV: Formalizing Penultimate Proxies for Efficient Concept Based Interpretability
Researchers have introduced E-TCAV, a new framework designed to enhance the efficiency of Testing with Concept Activation Vectors (TCAV), a popular method for interpreting neural networks. While TCAV effectively aligns internal network representations with human-understandable concepts, it traditionally suffers from high computational costs, statistical instability, and inconsistent scores across different layers. The E-TCAV framework addresses these limitations by utilizing the penultimate layer of neural networks as a fast proxy for earlier layers. Extensive evaluations across four architectures and five datasets in computer vision and natural language processing reveal that final block layers strongly agree with the penultimate layer regarding TCAV scores. Furthermore, the study attributes common score variance to the choice of latent classifiers. By leveraging inter-layer agreement and directional sensitivity degeneracy, E-TCAV achieves linearly scaling speed-ups relative to network size and sample count. This advancement facilitates more efficient model debugging and enables real-time concept-guided training, marking a significant step forward in making AI interpretability methods more practical and scalable for complex deep learning applications.
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