On Transfer in Classification: How Well do Subsets of Classes
  Generalize?
Raphael Baena Lucas Drumetz Vincent Gripon
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.