Comparing Deep Neural Nets with UMAP Tour

• URL: http://arxiv.org/abs/2110.09431v1
• Date: Mon, 18 Oct 2021 15:59:13 GMT
• Title: Comparing Deep Neural Nets with UMAP Tour
• Authors: Mingwei Li, Carlos Scheidegger
• Abstract summary: UMAP Tour is built to visually inspect and compare internal behavior of real-world neural network models. We find concepts learned in state-of-the-art models and dissimilarities between them, such as GoogLeNet and ResNet.
• Score: 12.910602784766562
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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