Characterizing Disparity Between Edge Models and High-Accuracy Base Models for Vision Tasks

• URL: http://arxiv.org/abs/2407.10016v1
• Date: Sat, 13 Jul 2024 22:05:58 GMT
• Title: Characterizing Disparity Between Edge Models and High-Accuracy Base Models for Vision Tasks
• Authors: Zhenyu Wang, Shahriar Nirjon,
• Abstract summary: We introduce XDELTA, a novel explainable AI tool that explains differences between a high-accuracy base model and a computationally efficient but lower-accuracy edge model. We conduct a comprehensive evaluation to test XDELTA's ability to explain model discrepancies, using over 1.2 million images and 24 models, and assessing real-world deployments with six participants.
• Score: 5.081175754775484
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Edge devices, with their widely varying capabilities, support a diverse range of edge AI models. This raises the question: how does an edge model differ from a high-accuracy (base) model for the same task? We introduce XDELTA, a novel explainable AI tool that explains differences between a high-accuracy base model and a computationally efficient but lower-accuracy edge model. To achieve this, we propose a learning-based approach to characterize the model difference, named the DELTA network, which complements the feature representation capability of the edge network in a compact form. To construct DELTA, we propose a sparsity optimization framework that extracts the essence of the base model to ensure compactness and sufficient feature representation capability of DELTA, and implement a negative correlation learning approach to ensure it complements the edge model. We conduct a comprehensive evaluation to test XDELTA's ability to explain model discrepancies, using over 1.2 million images and 24 models, and assessing real-world deployments with six participants. XDELTA excels in explaining differences between base and edge models (arbitrary pairs as well as compressed base models) through geometric and concept-level analysis, proving effective in real-world applications.

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