Prototype-based Dataset Comparison
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
BibTeX
@inproceedings{iccv2023_prototypebasedda,
title = {Prototype-based Dataset Comparison},
author = {Nanne van Noord},
booktitle = {ICCV 2023},
year = {2023}
}