In classification, it is usual to observe that models trained on a given set
of classes can generalize to previously unseen ones, suggesting the ability to
learn beyond the initial task. This ability is often leveraged in the context
of transfer learning where a pretrained model can be used to process new
classes, with or without fine tuning. Surprisingly, there are a few papers
looking at the theoretical roots beyond this phenomenon. In this work, we are
interested in laying the foundations of such a theoretical framework for
transferability between sets of classes. Namely, we establish a partially
ordered set of subsets of classes. This tool allows to represent which subset
of classes can generalize to others. In a more practical setting, we explore
the ability of our framework to predict which subset of classes can lead to the
best performance when testing on all of them. We also explore few-shot
learning, where transfer is the golden standard. Our work contributes to better
understanding of transfer mechanics and model generalization.

分类、迁移学习、部分有序集、类别的子集和模型泛化是本研究的关键词。研究旨在建立类别子集之间可迁移性的理论框架，并探索测试所有类别时最佳性能的预测能力。研究还关注迁移学习中的少样本学习，以提供对迁移机制和模型泛化的更好理解。