Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild. Despite their ubiquity in the real-world deployments, these distribution shifts are under-represented in the datasets widely used in the ML community today. To address this gap, we present WILDS, a curated benchmark of 10 datasets reflecting a diverse range of distribution shifts that naturally arise in real-world applications, such as shifts across hospitals for tumor identification; across camera traps for wildlife monitoring; and across time and location in satellite imaging and poverty mapping. On each dataset, we show that standard training yields substantially lower out-of-distribution than in-distribution performance. This gap remains even with models trained by existing methods for tackling distribution shifts, underscoring the need for new methods for training models that are more robust to the types of distribution shifts that arise in practice. To facilitate method development, we provide an open-source package that automates dataset loading, contains default model architectures and hyperparameters, and standardizes evaluations. Code and leaderboards are available at https://wilds.stanford.edu.
翻译:分布变化 -- -- 培训分布与测试分布不同 -- -- 可能大幅降低野生机学习系统(ML)的准确性。尽管这些分布变化在现实世界部署中普遍存在,但如今在ML社区广泛使用的数据集中代表性不足。为了弥补这一差距,我们提出了一个10个数据集的分类基准,反映了在现实世界应用中自然产生的分布变化的多样性,如肿瘤识别跨医院转移;野生动物监测跨相机陷阱;以及卫星成像和贫困绘图中的时间和地点。我们在每个数据集中显示,标准培训的分布结果远远低于分配绩效。即使这种差距还存在于通过现有方法培训的处理分布变化的模式中,强调新的培训模式对实际中出现的分布变化类型更加强大的需要。为了便利方法的开发,我们提供了一个开放源包,让自动化的数据集装入,包含默认模型架构和超参数,并使评价标准化。在 https://wilds.stand.edu. 中可以找到代码和领导板。