---
title: "SEE: Spherical Embedding Expansion for Improving Deep Metric Learning (Extended Abstract)"
authors:
  - "Binh M. Le"
  - "Simon S. Woo"
doi: "10.24963/ijcai.2025/1214"
url: "https://ijcai.org/proceedings/2025/1214"
pdf: "https://ijcai.org/proceedings/2025/1214.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "10906-10911"
track: "Sister Conferences Best Papers"
keywords:
  - "Sister Conferences Best Papers: Computer Vision"
  - "Sister Conferences Best Papers: Search"
---

# SEE: Spherical Embedding Expansion for Improving Deep Metric Learning (Extended Abstract)

Binh M. Le, Simon S. Woo

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 10906-10911. DOI: [10.24963/ijcai.2025/1214](https://doi.org/10.24963/ijcai.2025/1214)

## Abstract

The primary goal of deep metric learning is to construct a comprehensive embedding space that can effectively represent samples originating from both intra- and inter-classes. Although extensive prior work has explored diverse metric functions and innovative training strategies, much of this work relies on default training data. Consequently, the potential variations inherent within this data remain largely unexplored, constraining the model's robustness to unseen images. In this context, we introduce the  Spherical Embedding Expansion (SEE ) method. SEE aims to uncover the latent semantic variations in training data. Especially, our method augments the embedding space with synthetic representations based on Max-Mahalanobis distribution (MMD) centers, which maximize the dispersion of these synthetic features without increasing computational costs.We evaluated the efficacy of SEE on four renowned standard benchmarks for the image retrieval task. The results demonstrate that SEE consistently enhances the performance of conventional methods when integrated with them, setting a new benchmark for deep metric learning performance across all settings.