# Symmetrical Synthesis for Deep Metric Learning

arXiv: https://arxiv.org/abs/2001.11658 · HTML: https://mlsanity.com/paper/2001.11658/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jan 2020.

## Built on by (most cited first, 14 of 14)

- [Recall@k Surrogate Loss with Large Batches and Similarity Mixup](https://mlsanity.com/paper/2108.11179/) (2021)
- [It Takes Two to Tango: Mixup for Deep Metric Learning](https://mlsanity.com/paper/2106.04990/) (2021)
- [Learning with Memory-based Virtual Classes for Deep Metric Learning](https://mlsanity.com/paper/2103.16940/) (2021)
- [Proxy Synthesis: Learning with Synthetic Classes for Deep Metric Learning](https://mlsanity.com/paper/2103.15454/) (2021)
- [Granularity-aware Adaptation for Image Retrieval over Multiple Tasks](https://mlsanity.com/paper/2210.02254/) (2022)
- [VVS: Video-to-Video Retrieval with Irrelevant Frame Suppression](https://mlsanity.com/paper/2303.08906/) (2023)
- [Globally Correlation-Aware Hard Negative Generation](https://mlsanity.com/paper/2411.13145/) (2024)
- [Semi-Supervised Metric Learning: A Deep Resurrection](https://mlsanity.com/paper/2105.05061/) (2021)
- [A Framework to Enhance Generalization of Deep Metric Learning methods using General Discriminative Feature Learning and Class Adversarial Neural Networks](https://mlsanity.com/paper/2106.06420/) (2021)
- [Construct Informative Triplet with Two-stage Hard-sample Generation](https://mlsanity.com/paper/2112.02259/) (2021)
- [Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric Learning](https://mlsanity.com/paper/2211.16264/) (2022)
- [Learning to Generate Novel Classes for Deep Metric Learning](https://mlsanity.com/paper/2201.01008/) (2022)
- [Color Variants Identification in Fashion e-commerce via Contrastive Self-Supervised Representation Learning](https://mlsanity.com/paper/2104.08581/) (2021)
- [A Tale of Color Variants: Representation and Self-Supervised Learning in Fashion E-Commerce](https://mlsanity.com/paper/2112.02910/) (2021)

## Abstract (arXiv)

Deep metric learning aims to learn embeddings that contain semantic similarity information among data points. To learn better embeddings, methods to generate synthetic hard samples have been proposed. Existing methods of synthetic hard sample generation are adopting autoencoders or generative adversarial networks, but this leads to more hyper-parameters, harder optimization, and slower training speed. In this paper, we address these problems by proposing a novel method of synthetic hard sample generation called symmetrical synthesis. Given two original feature points from the same class, the proposed method firstly generates synthetic points with each other as an axis of symmetry. Secondly, it performs hard negative pair mining within the original and synthetic points to select a more informative negative pair for computing the metric learning loss. Our proposed method is hyper-parameter free and plug-and-play for existing metric learning losses without network modification. We demonstrate the superiority of our proposed method over existing methods for a variety of loss functions on clustering and image retrieval tasks. Our implementations is publicly available.