What is it about?

Distance metrics are functions that provide a way to quantify how far apart two elements of a given set are to each other. So, we propose a new data representation space called Similarity space (S-space) that separates regions where similar/dissimilar objects lie together and help the convergence of the model.

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Why is it important?

In our daily lives, we can identify several patterns in the world around us. Human perception (along with our inferential ability) observes these patterns and helps us to recognize common features among collections of objects. Thus, identifying common characteristics among objects becomes a critical task for understanding the world and the things surrounding it.


Writing this article was a great pleasure as it has co-authors with whom I have had long standing collaborations.

Pedro Henrique Barros
Federal University of Minas Gerais

Read the Original

This page is a summary of: A New Similarity Space Tailored for Supervised Deep Metric Learning, ACM Transactions on Intelligent Systems and Technology, November 2022, ACM (Association for Computing Machinery),
DOI: 10.1145/3559766.
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