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

翻译：数据集总结是数据检查的有效方法。然而，当应用于单一数据集时，视觉概念的发现仅限于最显著的概念。我们认为比较方法可以扩展这一范式，从而实现超越最显著概念的更丰富的数据检查形式。为了支持数据集比较，我们提出了一个跨数据集学习概念级原型的模块。我们利用自监督学习在无监督条件下发现这些原型，并通过两个案例研究展示了我们方法的优势。研究结果表明，数据集比较扩展了数据集检查的范围，我们希望鼓励更多相关方向的工作。代码和使用说明请访问 https://github.com/Nanne/ProtoSim