This paper addresses the problem of set-to-set matching, which involves matching two different sets of items based on some criteria, especially in the case of high-dimensional items like images. Although neural networks have been applied to solve this problem, most machine learning-based approaches assume that the training and test data follow the same distribution, which is not always true in real-world scenarios. To address this limitation, we introduce SHIFT15M, a dataset that can be used to evaluate set-to-set matching models when the distribution of data changes between training and testing. We conduct benchmark experiments that demonstrate the performance drop of naive methods due to distribution shift. Additionally, we provide software to handle the SHIFT15M dataset in a simple manner, with the URL for the software to be made available after publication of this manuscript. We believe proposed SHIFT15M dataset provide a valuable resource for evaluating set-to-set matching models under the distribution shift.

翻译：本文研究了集合匹配问题，即基于某些准则匹配两个不同项目集合（尤其是图像等高维项目集合）的任务。尽管神经网络已被应用于解决该问题，但大多数基于机器学习的方法假设训练数据和测试数据服从相同分布，这一假设在实际场景中往往不成立。为解决这一局限性，我们提出了SHIFT15M数据集，该数据集可用于评估训练与测试数据分布发生变化的集合匹配模型。我们通过基准实验证明，简单的匹配方法会因分布偏移而出现性能下降。此外，我们提供了简洁处理SHIFT15M数据集的软件工具，其访问链接将在本文发表后公开。我们认为，所提出的SHIFT15M数据集为评估分布偏移条件下的集合匹配模型提供了宝贵资源。