OCCAM: Towards Cost-Efficient and Accuracy-Aware Image Classification Inference

• URL: http://arxiv.org/abs/2406.04508v1
• Date: Thu, 6 Jun 2024 21:05:39 GMT
• Title: OCCAM: Towards Cost-Efficient and Accuracy-Aware Image Classification Inference
• Authors: Dujian Ding, Bicheng Xu, Laks V. S. Lakshmanan,
• Abstract summary: We propose a principled approach, OCCAM, to compute the best classifier assignment strategy over image classification queries. On a variety of real-world datasets, OCCAM achieves 40% cost reduction with little to no accuracy drop.
• Score: 11.267210747162961
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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