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 $\chi^2$ 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.

本研究针对神经网络在推理时面临的概念漂移问题，提出了一种应用卡方拟合优度假设检验的元算法，旨在适应不同的推理场景。通过对多层感知器、卷积神经网络和变换器的模拟实验，研究表明该方法能够检测到因概念漂移导致的准确率意外下降，从而提高模型在多变条件下的可靠性和安全性。

使用卡方拟合优度检验检测神经网络中的概念漂移