Neural networks should be interpretable to humans. In particular, there is a growing interest in concepts learned in a layer and similarity between layers. In this work, a tool, UMAP Tour, is built to visually inspect and compare internal behavior of real-world neural network models using well-aligned, instance-level representations. The method used in the visualization also implies a new similarity measure between neural network layers. Using the visual tool and the similarity measure, we find concepts learned in state-of-the-art models and dissimilarities between them, such as GoogLeNet and ResNet.
@article{arxiv.2110.09431,
title = {Comparing Deep Neural Nets with UMAP Tour},
author = {Mingwei Li and Carlos Scheidegger},
journal= {arXiv preprint arXiv:2110.09431},
year = {2021}
}