# Sharing Matters for Generalization in Deep Metric Learning

arXiv: https://arxiv.org/abs/2004.05582 · HTML: https://mlsanity.com/paper/2004.05582/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2020.

## Built on by (most cited first, 15 of 15)

- [Non-isotropy Regularization for Proxy-based Deep Metric Learning](https://mlsanity.com/paper/2203.08547/) (2022)
- [Learning with Memory-based Virtual Classes for Deep Metric Learning](https://mlsanity.com/paper/2103.16940/) (2021)
- [Integrating Language Guidance into Vision-based Deep Metric Learning](https://mlsanity.com/paper/2203.08543/) (2022)
- [A Non-isotropic Probabilistic Take on Proxy-based Deep Metric Learning](https://mlsanity.com/paper/2207.03784/) (2022)
- [Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning](https://mlsanity.com/paper/2107.09562/) (2021)
- [SCVRL: Shuffled Contrastive Video Representation Learning](https://mlsanity.com/paper/2205.11710/) (2022)
- [Improved Diversity-Promoting Collaborative Metric Learning for Recommendation](https://mlsanity.com/paper/2409.01012/) (2024)
- [Contrastive Bayesian Analysis for Deep Metric Learning](https://mlsanity.com/paper/2210.04402/) (2022)
- [InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for Images](https://mlsanity.com/paper/2211.12760/) (2022)
- [Rethinking the Optimization of Average Precision: Only Penalizing Negative Instances before Positive Ones is Enough](https://mlsanity.com/paper/2102.04640/) (2021)
- [HPCR: Holistic Proxy-based Contrastive Replay for Online Continual Learning](https://mlsanity.com/paper/2309.15038/) (2023)
- [Multi-threshold Deep Metric Learning for Facial Expression Recognition](https://mlsanity.com/paper/2406.16434/) (2024)
- [Center Prediction Loss for Re-identification](https://mlsanity.com/paper/2104.14746/) (2021)
- [Catching Image Retrieval Generalization](https://mlsanity.com/paper/2306.13357/) (2023)
- [Beyond Seen Bounds: Class-Centric Polarization for Single-Domain Generalized Deep Metric Learning](https://mlsanity.com/paper/2601.09121/) (2026)

## Abstract (arXiv)

Learning the similarity between images constitutes the foundation for numerous vision tasks. The common paradigm is discriminative metric learning, which seeks an embedding that separates different training classes. However, the main challenge is to learn a metric that not only generalizes from training to novel, but related, test samples. It should also transfer to different object classes. So what complementary information is missed by the discriminative paradigm? Besides finding characteristics that separate between classes, we also need them to likely occur in novel categories, which is indicated if they are shared across training classes. This work investigates how to learn such characteristics without the need for extra annotations or training data. By formulating our approach as a novel triplet sampling strategy, it can be easily applied on top of recent ranking loss frameworks. Experiments show that, independent of the underlying network architecture and the specific ranking loss, our approach significantly improves performance in deep metric learning, leading to new the state-of-the-art results on various standard benchmark datasets. Preliminary early access page can be found here: https://ieeexplore.ieee.org/document/9141449