识别测试基准和生产数据之间的上下文转换
Identifying the Context Shift between Test Benchmarks and Production Data
摘要：
机器学习模型在生产数据上通常很脆弱，尽管在基准数据集上实现了高精度。传统上，基准数据集有两个目的：首先，基准提供了一个标准，机器学习研究人员可以在此基础上比较不同的方法，其次，基准提供了一个现实世界的模型，尽管不完美。测试基准（以及模型训练所依据的数据）的不完整性阻碍了机器学习的鲁棒性，实现了快捷学习，并使模型系统地容易在分布外和不利扰动的数据上出错。单个静态基准数据集和生产数据集之间的不匹配传统上被描述为数据集偏移。为了澄清如何解决测试基准和生产数据之间的不匹配，我们引入上下文转移来描述底层数据生成过程中语义上有意义的变化。此外，我们确定了三种方法来解决上下文转移，否则会导致模型预测错误：首先，我们描述了人类的直觉和专家知识如何识别模型系统失败的语义上有意义的特征，其次，我们详细说明了动态基准测试-其重点是捕获数据生成过程-如何通过确证来促进概括性，第三，我们强调，澄清模型的局限性可以减少意外的错误。稳健的机器学习专注于超越基准的模型性能，因此，我们考虑了三个模型生物领域-面部表情识别，deepfake检测和医疗诊断-以强调基准任务中的隐含假设如何导致实践中的错误。通过密切关注上下文的作用，研究者可以设计更全面的基准，减少上下文转换错误，并增加概括性。
英文摘要：
Machine learning models are often brittle on production data despite achieving high accuracy on benchmark datasets. Benchmark datasets have traditionally served dual purposes: first, benchmarks offer a standard on which machine learning researchers can compare different methods, and second, benchmarks provide a model, albeit imperfect, of the real world. The incompleteness of test benchmarks (and the data upon which models are trained) hinder robustness in machine learning, enable shortcut learning, and leave models systematically prone to err on out-of-distribution and adversarially perturbed data. The mismatch between a single static benchmark dataset and a production dataset has traditionally been described as a dataset shift. In an effort to clarify how to address the mismatch between test benchmarks and production data, we introduce context shift to describe semantically meaningful changes in the underlying data generation process. Moreover, we identify three methods for addressing context shift that would otherwise lead to model prediction errors: first, we describe how human intuition and expert knowledge can identify semantically meaningful features upon which models systematically fail, second, we detail how dynamic benchmarking - with its focus on capturing the data generation process - can promote generalizability through corroboration, and third, we highlight that clarifying a model's limitations can reduce unexpected errors. Robust machine learning is focused on model performance beyond benchmarks, and as such, we consider three model organism domains - facial expression recognition, deepfake detection, and medical diagnosis - to highlight how implicit assumptions in benchmark tasks lead to errors in practice. By paying close attention to the role of context, researchers can design more comprehensive benchmarks, reduce context shift errors, and increase generalizability.
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