部署在野外的机器学习系统通常在源分布上培训，但部署在不同的目标分布上。未标记的数据可以是用于缓解这些分布班次的强大的利用点，因为它通常比标记数据更具可用。然而，未标记数据的现有分配转换基准不反映现实世界应用中出现的方案的广度。在这项工作中，我们介绍了Wilds 2.0更新，该更新在分发转移的野外基准中扩展了10个数据集中的8个，以包括将在部署中逼真获得的策划未标记数据。为了保持一致性，标记的培训，验证和测试集以及评估度量与原始野外基准中的标记与评估度量完全相同。这些数据集涵盖了广泛的应用程序（从组织学到野生动物保护），任务（分类，回归和检测）和方式（照片，卫星图像，显微镜载玻片，文本，分子图）。我们系统地基准测试最先进的方法，可以利用未标记的数据，包括域不变，自我培训和自我监督方法，并表明他们在野外的成功2.0是有限的。为了方便方法开发和评估，我们提供了一个自动化数据加载的开源包，并包含本文中使用的所有模型架构和方法。代码和排行榜可在https://wilds.stanford.edu获得。

AI正在经历范式转变，随着模型的兴起（例如Bert，Dall-E，GPT-3），这些模型经过大规模的数据训练，并且可以适应广泛的下游任务。我们称这些模型基础模型来强调其至关重要但不完整的特征。该报告提供了基础模型的机会和风险的详尽说明，包括其功能（例如语言，愿景，机器人技术，推理，人类互动）和技术原则（例如，模型架构，培训程序，数据，系统，安全，安全性，评估，理论）对其应用（例如法律，医疗保健，教育）和社会影响（例如不平等，滥用，经济和环境影响，法律和道德考虑）。尽管基础模型基于标准的深度学习和转移学习，但它们的规模导致了新的新兴能力，以及它们在许多任务中的有效性都激发了同质化。同质化提供了强大的杠杆作用，但要求谨慎，因为基础模型的缺陷均由下游的所有适应模型继承。尽管即将广泛地部署基础模型，但我们目前对它们的工作方式，失败以及由于其新兴属性的影响而缺乏清晰的了解。为了解决这些问题，我们认为基础模型的许多批判性研究都需要与他们的基本社会技术性质相称。

Standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on certain groups, especially in the presence of spurious correlations between the input and label. Prior approaches that achieve high worst-group accuracy, like group distributionally robust optimization (group DRO) require expensive group annotations for each training point, whereas approaches that do not use such group annotations typically achieve unsatisfactory worst-group accuracy. In this paper, we propose a simple two-stage approach, JTT, that first trains a standard ERM model for several epochs, and then trains a second model that upweights the training examples that the first model misclassified. Intuitively, this upweights examples from groups on which standard ERM models perform poorly, leading to improved worst-group performance. Averaged over four image classification and natural language processing tasks with spurious correlations, JTT closes 75% of the gap in worst-group accuracy between standard ERM and group DRO, while only requiring group annotations on a small validation set in order to tune hyperparameters.

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.

Overparameterized neural networks can be highly accurate on average on an i.i.d.test set yet consistently fail on atypical groups of the data (e.g., by learning spurious correlations that hold on average but not in such groups). Distributionally robust optimization (DRO) allows us to learn models that instead minimize the worst-case training loss over a set of pre-defined groups. However, we find that naively applying group DRO to overparameterized neural networks fails: these models can perfectly fit the training data, and any model with vanishing average training loss also already has vanishing worst-case training loss. Instead, the poor worst-case performance arises from poor generalization on some groups. By coupling group DRO models with increased regularization-a stronger-than-typical 2 penalty or early stopping-we achieve substantially higher worst-group accuracies, with 10-40 percentage point improvements on a natural language inference task and two image tasks, while maintaining high average accuracies. Our results suggest that regularization is important for worst-group generalization in the overparameterized regime, even if it is not needed for average generalization. Finally, we introduce a stochastic optimization algorithm, with convergence guarantees, to efficiently train group DRO models.

当源和目标域之间存在较大的差距时，常规域的适应方法无法正常工作。逐渐的域适应性是通过利用中间域来解决问题的方法之一，该域逐渐从源源转移到目标域。先前的工作假设中间域的数量很大，并且相邻域的距离很小。因此，适用于未标记的数据集通过自我训练的逐渐域适应算法。但是，实际上，由于中间域的数量有限，并且相邻域的距离很大，因此逐渐的自我训练将失败。我们建议使用归一化流量来减轻此问题，同时保持无监督域适应的框架。我们通过标准化流量生成伪中间域，然后将其用于逐渐的域适应性。我们通过使用现实世界数据集的实验来评估我们的方法，并确认我们提出的方法减轻了上述解释的问题并改善了分类性能。

在域适应性中，当源和目标域之间存在较大距离时，预测性能将降低。假设我们可以访问中间域，从源逐渐从源转移到目标域，则逐渐的域适应性是解决此类问题的解决方案之一。在以前的工作中，假定中间域中的样品数量足够大。因此，无需标记数据就可以进行自我训练。如果限制了可访问的中间域的数量，则域之间的距离变得很大，并且自我训练将失败。实际上，中间域中样品的成本会有所不同，自然可以考虑到中间域越接近目标域，从中间域中获得样品的成本就越高。为了解决成本和准确性之间的权衡，我们提出了一个结合了多重率和主动领域适应性的框架。通过使用现实世界数据集的实验来评估所提出方法的有效性。