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arXiv · 1901.00536

Visualizing Deep Similarity Networks

Abby Stylianou·Richard Souvenir·Robert Pless
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Abstract

For convolutional neural network models that optimize an image embedding, we propose a method to highlight the regions of images that contribute most to pairwise similarity. This work is a corollary to the visualization tools developed for classification networks, but applicable to the problem domains better suited to similarity learning. The visualization shows how similarity networks that are fine-tuned learn to focus on different features. We also generalize our approach to embedding networks that use different pooling strategies and provide a simple mechanism to support image similarity searches on objects or sub-regions in the query image.

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Abby Stylianou, Richard Souvenir, Robert Pless. 2019-01-02. Visualizing Deep Similarity Networks. https://arxiv.org/abs/1901.00536

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