OCCAM: Towards Cost-Efficient and Accuracy-Aware Image Classification Inference
<|reference_start|>OCCAM: Towards Cost-Efficient and Accuracy-Aware Image Classification Inference: Image classification is a fundamental building block for a majority of computer vision applications. With the growing popularity and capacity of machine learning models, people can easily access trained image classifiers as a service online or offline. However, model use comes with a cost and classifiers of higher capacity usually incur higher inference costs. To harness the respective strengths of different classifiers, we propose a principled approach, OCCAM, to compute the best classifier assignment strategy over image classification queries (termed as the optimal model portfolio) so that the aggregated accuracy is maximized, under user-specified cost budgets. Our approach uses an unbiased and low-variance accuracy estimator and effectively computes the optimal solution by solving an integer linear programming problem. On a variety of real-world datasets, OCCAM achieves 40% cost reduction with little to no accuracy drop.<|reference_end|>
@article{ding2024occam:,
    title={OCCAM: Towards Cost-Efficient and Accuracy-Aware Image Classification
  Inference},
    author={Dujian Ding, Bicheng Xu, Laks V.S. Lakshmanan},
    journal={arXiv preprint arXiv:2406.04508},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04508},
    primaryClass={cs.CV cs.AI cs.LG}
}