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Accuracy, limits, and approximation

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Accuracy and Precision01:52

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Related Experiment Videos

The accuracy-fairness-efficiency Trilemma in mobile image classification: a Pareto benchmark.

Thanh TranVan1, Hien LamThanh2, Toan DoNang3

  • 1Faculty of Mechatronics and Electronics, Lac Hong University, Dongnai, 76120, Vietnam.

Scientific Reports
|June 4, 2026
PubMed
Summary

This study introduces a framework for optimizing deep learning models on mobile devices, balancing accuracy, fairness, and efficiency. The best strategy, combining 3D augmentation and Protected Fairness Pruning, achieves optimal performance within deployment constraints.

Keywords:
3D data augmentationDemographic fairnessEdge deploymentImage classificationKnowledge distillationModel compressionMulti-objective optimizationPareto frontierQuantization

Related Experiment Videos

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Deploying deep learning on mobile devices faces challenges balancing accuracy, fairness, and efficiency.
  • Existing methods lack a unified framework for these competing objectives.
  • No prior work defined a Deployment-Feasible Zone (DFZ) for constrained optimization.

Purpose of the Study:

  • To formalize the deployment of deep learning on mobile devices as a constrained multi-objective optimization problem.
  • To benchmark various optimization strategies under identical, resource-constrained conditions.
  • To define and identify the Deployment-Feasible Zone (DFZ) representing optimal trade-offs.

Main Methods:

  • Benchmarking eleven deep learning optimization configurations on a 2,821-image dataset.
  • Evaluating performance across 24 demographic subgroups under strict constraints (accuracy, fairness, memory, latency).
  • Utilizing 3D-aware augmentation and Protected Fairness Pruning (PFP), alongside an Adaptive Trilemma Weight Scheduler (ATWS).

Main Results:

  • The combination of 3D-aware augmentation and PFP (C2) emerged as the Pareto knee point, achieving high accuracy (0.906-0.962), fairness, 6.3 MB size, and 187 ms inference time.
  • Fairness-constrained pruning consistently outperformed standard magnitude pruning at similar compression ratios.
  • The ATWS strategy improved fairness metrics by 1.3 pp and 0.7 pp over fixed-weight training.

Conclusions:

  • A novel framework and the concept of a Deployment-Feasible Zone (DFZ) are established for mobile deep learning deployment.
  • The optimal strategy (C2) demonstrates a viable approach to satisfy accuracy, fairness, and efficiency constraints simultaneously.
  • Adaptive scheduling methods offer significant improvements in fairness over static approaches.