用卡方检验检测神经网络推理中的概念漂移，无需查看输出。
Detecting Concept Drift in Neural Networks Using Chi-squared Goodness of Fit Testing
- 用卡方拟合优度检验监测模型输出分布变化。
- 在多种视觉模型上成功检测到模拟漂移导致的性能下降。
- 适合需要持续可靠性验证的部署场景。
随着深度学习模型应用范围超越人工验证能力，亟需元算法保障模型推理可靠性。概念漂移检测旨在识别统计分布的变化，但在监控神经网络时仍被低估，尤其当模型面对与训练数据分布不同的推理数据时。针对不同模型架构、应用场景和数据集，概念漂移检测算法需具备适应性。本文将χ²拟合优度假设检验作为元算法，应用于多层感知机、卷积神经网络和用于机器视觉的Transformer，在推理过程中引入模拟漂移。结果表明，可无需直接分析推理输出，即可检测由概念漂移引发的意外准确率下降。该方法通过持续评估模型在不同条件下的可靠性，提升系统安全性。
原文摘要 · Abstract (English)
As the adoption of deep learning models has grown beyond human capacity for verification, meta-algorithms are needed to ensure reliable model inference. Concept drift detection is a field dedicated to identifying statistical shifts that is underutilized in monitoring neural networks that may encounter inference data with distributional characteristics diverging from their training data. Given the wide variety of model architectures, applications, and datasets, it is important that concept drift detection algorithms are adaptable to different inference scenarios. In this paper, we introduce an application of the $χ^2$ Goodness of Fit Hypothesis Test as a drift detection meta-algorithm applied to a multilayer perceptron, a convolutional neural network, and a transformer trained for machine vision as they are exposed to simulated drift during inference. To that end, we demonstrate how unexpected drops in accuracy due to concept drift can be detected without directly examining the inference outputs. Our approach enhances safety by ensuring models are continually evaluated for reliability across varying conditions.
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