SOFTWARE ENGINEERING & METHODOLOGY
PriCod: Prioritizing Test Inputs for Compressed Deep Neural Networks
This groundbreaking research introduces PriCod, a novel test prioritization approach specifically designed for compressed Deep Neural Networks (DNNs). By leveraging behavioral disparities caused by model compression and test input embeddings, PriCod efficiently identifies and prioritizes potentially misclassified tests. This significantly reduces the manual labeling effort and accelerates bug detection in resource-constrained AI deployments.
Accelerating AI Reliability & Reducing Costs
PriCod delivers tangible benefits for enterprises deploying compressed DNNs, particularly in resource-constrained environments.
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Avg. Improvement on Natural Inputs (APFD)
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Avg. Improvement on Noisy Inputs (APFD)
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Avg. Improvement on Adversarial Inputs (APFD)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Core Innovation
Methodology
Performance Gains
Real-World Impact
Unlocking Efficiency: PriCod's Core Innovation
At its heart, PriCod identifies critical test inputs by analyzing the 'deviation' between the original, uncompressed DNN model and its compressed counterpart. This unique approach pinpoints inputs where compression likely introduced vulnerabilities, making testing far more targeted and efficient.
55.89% Max APFD Improvement over Random (Natural Inputs)Enterprise Process Flow
Original & Compressed Model Output Comparison
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Deviation Feature Generation
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Embedding Feature Generation
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Feature Fusion
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Misclassification Probability Prediction
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Prioritized Test Set Ranking
PriCod vs. Traditional Methods: A Performance Benchmark
Compared against state-of-the-art test prioritization techniques, PriCod consistently demonstrates superior effectiveness in identifying misclassified tests across various input types. The table below highlights key advantages.
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| Average APFD Improvement (Natural) |
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Case Study: Enhancing Medical Image Diagnosis with PriCod
In a critical medical application involving lung disease diagnosis from X-ray images, a compressed DNN model was deployed on mobile devices. Initially, accuracy loss due to compression led to potential misdiagnoses. By implementing PriCod, high-risk images (those most likely to be misclassified by the compressed model) were prioritized for screening. This led to a significant reduction in misdiagnosis risk and enhanced reliability of the diagnostic model, ensuring earlier and more accurate interventions.
Reduced Misdiagnosis Risk
By prioritizing critical inputs, PriCod significantly lowered the chances of misdiagnosis in compressed medical imaging models, ensuring more reliable outcomes for patients.
Enhanced Model Reliability
The ability to quickly identify and address potential misclassifications improved the overall trustworthiness and performance of the compressed DNN in a sensitive healthcare context.
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