用多项式混沌分析图像分类模型对输入不确定性的敏感度。
Sensitivity analysis of image classification models using generalized polynomial chaos
- 将输入域偏移建模为随机变量，通过广义多项式混沌计算敏感度。
- 在焊缝缺陷与宝马车标识别任务中验证，可量化不确定性影响。
- 适合关注模型可信度与鲁棒性的工业视觉应用研究者。
在生产中集成先进通信协议加速了数据驱动预测质量方法的应用，尤其是机器学习（ML）模型。然而，图像分类模型常因模型、数据及领域偏移产生显著不确定性，导致输出过度自信。为更好理解这些模型，敏感性分析可揭示输入参数对输出的影响程度。本文研究用于预测质量的图像分类模型的敏感性，提出将输入分布域偏移建模为随机变量，并利用广义多项式混沌（GPC）计算的Sobol指数量化其对模型输出的影响。该方法通过焊接缺陷分类案例验证，采用微调的ResNet18模型和宝马集团生产线使用的车标分类模型。
原文摘要 · Abstract (English)
Integrating advanced communication protocols in production has accelerated the adoption of data-driven predictive quality methods, notably machine learning (ML) models. However, ML models in image classification often face significant uncertainties arising from model, data, and domain shifts. These uncertainties lead to overconfidence in the classification model's output. To better understand these models, sensitivity analysis can help to analyze the relative influence of input parameters on the output. This work investigates the sensitivity of image classification models used for predictive quality. We propose modeling the distributional domain shifts of inputs with random variables and quantifying their impact on the model's outputs using Sobol indices computed via generalized polynomial chaos (GPC). This approach is validated through a case study involving a welding defect classification problem, utilizing a fine-tuned ResNet18 model and an emblem classification model used in BMW Group production facilities.
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