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The notion of task similarity is at the core of various machine learningparadigms, such as domain adaptation and meta-learning. Current methods toquantify it are often heuristic, make strong assumptions on the label setsacross the tasks, and many are architecture-dependent, relying on task-specificoptimal parameters (e.g., require training a model on each dataset). In thiswork we propose an alternative notion of distance between datasets that (i) ismodel-agnostic, (ii) does not involve training, (iii) can compare datasets evenif their label sets are completely disjoint and (iv) has solid theoreticalfooting. This distance relies on optimal transport, which provides it with richgeometry awareness, interpretable correspondences and well-understoodproperties. Our results show that this novel distance provides meaningfulcomparison of datasets, and correlates well with transfer learning hardnessacross various experimental settings and datasets.

Geometric Dataset Distances via Optimal Transport