论文标题
深度学习分类器中的顺序漂移检测
Sequential Drift Detection in Deep Learning Classifiers
论文作者
论文摘要
我们利用神经网络嵌入来检测数据漂移，通过在适当的顺序决策框架内制定漂移检测。尽管统计检验反复应用，但可以控制错误的警报率。由于变更检测算法自然会在避免错误警报和快速正确检测之间面临权衡，因此我们引入了损失功能，该功能评估了算法平衡这两个问题的能力，并且我们在一系列实验中使用了算法。
We utilize neural network embeddings to detect data drift by formulating the drift detection within an appropriate sequential decision framework. This enables control of the false alarm rate although the statistical tests are repeatedly applied. Since change detection algorithms naturally face a tradeoff between avoiding false alarms and quick correct detection, we introduce a loss function which evaluates an algorithm's ability to balance these two concerns, and we use it in a series of experiments.
