# Classification is a Strong Baseline for Deep Metric Learning

arXiv: https://arxiv.org/abs/1811.12649 · HTML: https://mlsanity.com/paper/1811.12649/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

## Built on by (most cited first, 8 of 8)

- [Robust and Decomposable Average Precision for Image Retrieval](https://mlsanity.com/paper/2110.01445/) (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)
- [BT2: Backward-compatible Training with Basis Transformation](https://mlsanity.com/paper/2211.03989/) (2022)
- [Improving Deep Metric Learning by Divide and Conquer](https://mlsanity.com/paper/2109.04003/) (2021)
- [adSformers: Personalization from Short-Term Sequences and Diversity of Representations in Etsy Ads](https://mlsanity.com/paper/2302.01255/) (2023)
- [Three Things to Know about Deep Metric Learning](https://mlsanity.com/paper/2412.12432/) (2024)
- [Region-Aware CLS Token Augmentation for Fine-Grained Image Retrieval](https://mlsanity.com/paper/2610.10991/) (2026)

## Abstract (arXiv)

Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images. Two major applications of metric learning are content-based image retrieval and face verification. For the retrieval tasks, the majority of current state-of-the-art (SOTA) approaches are triplet-based non-parametric training. For the face verification tasks, however, recent SOTA approaches have adopted classification-based parametric training. In this paper, we look into the effectiveness of classification based approaches on image retrieval datasets. We evaluate on several standard retrieval datasets such as CAR-196, CUB-200-2011, Stanford Online Product, and In-Shop datasets for image retrieval and clustering, and establish that our classification-based approach is competitive across different feature dimensions and base feature networks. We further provide insights into the performance effects of subsampling classes for scalable classification-based training, and the effects of binarization, enabling efficient storage and computation for practical applications.