Testing Neural Networks via Bayesian-Guided Exploration of Decision Landscapes

2026-06-04 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Expert, extended

Summary

BayesWarp is a novel white-box testing framework designed to enhance the reliability of neural networks by efficiently uncovering diverse model failures. It addresses limitations of traditional global mutation or coverage-guided strategies by focusing mutations on decision-critical input regions, identified through interpretable saliency techniques. The framework adaptively guides its testing process using an uncertainty-aware Bayesian Optimization strategy, ensuring discovered failures remain distributionally and semantically proximate to original data. Evaluated on MNIST, CIFAR-10, and ImageNet across six neural network models, BayesWarp consistently improves failure discovery, failure diversity, test case quality, and critical neuron coverage within a fixed 10,000-mutation budget per input seed. Furthermore, fine-tuning models with the failure cases generated by BayesWarp leads to measurable improvements in model performance.

Key takeaway

For Machine Learning Engineers deploying neural networks in safety-critical domains, traditional testing methods often fall short in efficiently uncovering diverse, semantically relevant failures. You should consider integrating BayesWarp's approach, which focuses mutations on decision-critical input regions using Bayesian optimization. This strategy not only yields a broader spectrum of meaningful failure cases but also allows you to fine-tune your models with these specific examples, directly improving overall test accuracy and model robustness.

Key insights

BayesWarp efficiently uncovers diverse neural network failures by localizing mutations to decision-critical regions via Bayesian optimization.

Principles

• Focus DNN testing on decision-critical input regions, not global coverage.
• Balance exploration and exploitation in testing to find diverse failure modes.
• Maintain data distribution and semantic proximity during test case generation.

Method

BayesWarp localizes decision-critical regions using saliency maps, defines a diversity-oriented objective with adaptive weighting, and employs grid-parameterized SVGP Bayesian optimization for uncertainty-aware mutation guidance.

In practice

• Apply saliency techniques to pinpoint decision-critical input areas for mutation.
• Integrate Bayesian optimization to guide test case generation efficiently.
• Retrain models with BayesWarp-discovered failures to enhance robustness.

Topics

• Neural Network Testing
• Bayesian Optimization
• Saliency Maps
• White-box Testing
• Failure Diversity
• Model Reliability

Code references

• beanduan22/BayesWarp_

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.