The problem of matching two sets of multiple elements, namely set-to-set matching, has received a great deal of attention in recent years. In particular, it has been reported that good experimental results can be obtained by preparing a neural network as a matching function, especially in complex cases where, for example, each element of the set is an image. However, theoretical analysis of set-to-set matching with such black-box functions is lacking. This paper aims to perform a generalization error analysis in set-to-set matching to reveal the behavior of the model in that task.

翻译：近年来，多元素集合间的匹配问题（即集合到集合匹配）受到了广泛关注。特别是在复杂场景下（例如集合中每个元素均为图像时），通过构建神经网络作为匹配函数能够获得良好的实验效果。然而，现有研究缺乏对此类黑箱函数在集合到集合匹配任务中的理论分析。本文旨在对集合到集合匹配进行泛化误差分析，以揭示该任务中模型的行为特性。