Characterizing Disparity Between Edge Models and High-Accuracy Base Models for Vision Tasks
<|reference_start|>Characterizing Disparity Between Edge Models and High-Accuracy Base Models for Vision Tasks: 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.<|reference_end|>
@article{wang2024characterizing,
    title={Characterizing Disparity Between Edge Models and High-Accuracy Base
  Models for Vision Tasks},
    author={Zhenyu Wang, Shahriar Nirjon},
    journal={arXiv preprint arXiv:2407.10016},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10016},
    primaryClass={cs.CV cs.AI}
}