Despite their successful application to a variety of tasks, neural networks remain limited, like other machine learning methods, by their sensitivity to shifts in the data: their performance can be severely impacted by differences in distribution between the data on which they were trained and that on which they are deployed. In this article, we propose a new family of representations, called MAGDiff, that we extract from any given neural network classifier and that allows for efficient covariate data shift detection without the need to train a new model dedicated to this task. These representations are computed by comparing the activation graphs of the neural network for samples belonging to the training distribution and to the target distribution, and yield powerful data- and task-adapted statistics for the two-sample tests commonly used for data set shift detection. We demonstrate this empirically by measuring the statistical powers of two-sample Kolmogorov-Smirnov (KS) tests on several different data sets and shift types, and showing that our novel representations induce significant improvements over a state-of-the-art baseline relying on the network output.

翻译：尽管神经网络已成功应用于多种任务，但与其他机器学习方法一样，它们对数据偏移的敏感性仍构成局限：训练数据与部署数据之间的分布差异会严重影响其性能。本文提出一种名为MAGDiff的新型表示族，该表示族可从任意给定神经网络分类器中提取，无需训练专用于偏移检测的新模型即可高效实现协变量数据偏移检测。这种表示通过比较神经网络在训练分布与目标分布样本上的激活图来生成，为数据集偏移检测中常用的双样本检验提供了数据自适应和任务自适应的强效统计量。我们通过在不同数据集和偏移类型上测量双样本Kolmogorov-Smirnov检验的统计功效验证了这一方法的有效性，结果表明，相较依赖网络输出的现有最优基线方法，我们提出的新型表示能实现显著性能提升。