Prototype-based Dataset Comparison

TL;DR

This paper introduces a self-supervised prototype-based method for comparing datasets, enabling richer inspection beyond prominent concepts, demonstrated through two case studies.

Contribution

It presents a novel module that learns concept-level prototypes across datasets using self-supervised learning, enhancing dataset comparison capabilities.

Findings

Dataset comparison extends dataset inspection.

Prototype learning uncovers diverse visual concepts.

Method benefits demonstrated in two case studies.

Abstract

Dataset summarisation is a fruitful approach to dataset inspection. However, when applied to a single dataset the discovery of visual concepts is restricted to those most prominent. We argue that a comparative approach can expand upon this paradigm to enable richer forms of dataset inspection that go beyond the most prominent concepts. To enable dataset comparison we present a module that learns concept-level prototypes across datasets. We leverage self-supervised learning to discover these prototypes without supervision, and we demonstrate the benefits of our approach in two case-studies. Our findings show that dataset comparison extends dataset inspection and we hope to encourage more works in this direction. Code and usage instructions available at https://github.com/Nanne/ProtoSim