Representational similarity measures are essential instruments in machine learning as they facilitate the comparison of internal representations of neural networks, aiding researchers in understanding how various neural network layers and architectures process information. These measures are vital for understanding the performance, behavior, and learning dynamics of a model. However, the development and application of these measures often lack consistency due to the lack of calculated comparisons to other methods. This is compounded by the various tasks handled by the myriad of neural network architectures. Resolving these deficiencies requires a comprehensive benchmark to facilitate a consistent evaluation framework.
Addressing these challenges, researchers from various universities including the University of Passau, German Cancer Research Center (DKFZ), and the University of Heidelberg, have introduced the Representational Similarity (ReSi) benchmark, the first comprehensive, extensible platform devised to appraise representational similarity measures.
The ReSi benchmark includes six well-defined tests, 23 similarity measures, 11 neural network architectures, and six datasets. The meticulously designed benchmark is meant to evaluate a range of scenarios to provide a solid foundation for comparing the performance of different similarity measures. The domains evaluated range from graph, language, and vision, and use varying neural network architectures such as BERT, ResNet, and VGG.
The ReSi benchmark’s findings reveal that no single similarity measure consistently excels across all domains; for example, second-order cosine and Jaccard similarities were effective in the graph domain, angle-based measures performed well for language models, while the Centered Kernel Alignment (CKA) was successful for vision models. Hence, the benchmark has spotlighted the strengths and weaknesses of different measures, giving researchers valuable insights into the selection of the best measure for specific needs.
The benchmark’s wide variety of tests, models, and datasets allows for an extensive examination of the strengths and limitations of each similarity measure. Detailed findings from the ReSi benchmark highlight the complexity of evaluating representational similarity and the importance of conducting a comprehensive benchmark like ReSi to understand each measure’s performance thoroughly.
To conclude, the ReSi benchmark addresses a crucial gap in representational similarity measures evaluation by providing a methodical and robust platform. It advances machine learning research by facilitating more rigorous and consistent evaluations of similarity measures across different domains and architectures. The benchmark’s ability to be scaled up to include new measures, models, and tests also leaves the door open for future research.
