Distribution shifts between training and deployment data often affect the performance of machine learning models. In this paper, we explore a setting where a hidden variable induces a shift in the distribution of classes. These distribution shifts are particularly challenging for zero-shot classifiers, as they rely on representations learned from training classes, but are deployed on new, unseen ones. We introduce an algorithm to learn data representations that are robust to such class distribution shifts in zero-shot verification tasks. We show that our approach, which combines hierarchical data sampling with out-of-distribution generalization techniques, improves generalization to diverse class distributions in both simulations and real-world datasets.

翻译：训练数据与部署数据之间的分布偏移时常影响机器学习模型的性能。本文探讨了隐藏变量引发类别分布偏移的场景。这类分布偏移对零样本分类器尤为棘手，因其依赖从训练类别学习到的表征，却要在未见的新类别上部署。我们提出一种算法，用于学习对零样本验证任务中此类类别分布偏移具有鲁棒性的数据表征。实验表明，通过将层次化数据采样与分布外泛化技术相结合，该方法在模拟数据集和真实数据集上均能提升对多样化类别分布的泛化能力。