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arXiv · 2203.16475

Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries

Haekyu Park·Seongmin Lee·Benjamin Hoover·Austin P. Wright·Omar Shaikh·Rahul Duggal·Nilaksh Das·Kevin Li·Judy Hoffman·Duen Horng Chau
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Abstract

We present ConceptEvo, a unified interpretation framework for deep neural networks (DNNs) that reveals the inception and evolution of learned concepts during training. Our work addresses a critical gap in DNN interpretation research, as existing methods primarily focus on post-training interpretation. ConceptEvo introduces two novel technical contributions: (1) an algorithm that generates a unified semantic space, enabling side-by-side comparison of different models during training, and (2) an algorithm that discovers and quantifies important concept evolutions for class predictions. Through a large-scale human evaluation and quantitative experiments, we demonstrate that ConceptEvo successfully identifies concept evolutions across different models, which are not only comprehensible to humans but also crucial for class predictions. ConceptEvo is applicable to both modern DNN architectures, such as ConvNeXt, and classic DNNs, such as VGGs and InceptionV3.

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Haekyu Park, Seongmin Lee, Benjamin Hoover, Austin P. Wright, Omar Shaikh, Rahul Duggal, Nilaksh Das, Kevin Li, Judy Hoffman, Duen Horng Chau. 2022-03-30. Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries. https://arxiv.org/abs/2203.16475

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