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๐ A curated list of awesome practical Metric Learning and its applications
| Date | Stars |
|---|---|
| 2026-07-24 | 524 |
| 2026-07-25 | 524 |
| 2026-07-28 | 524 |
| 2026-07-30 | 524 |
| 2026-08-06 | 524 |
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# awesome-metric-learning
๐ Awesome list about practical Metric Learning and its applications
## Motivation ๐ค
At Qdrant, we have one goal: make metric learning more practical. This listing is in line with this purpose, and we aim at providing a concise yet useful list of awesomeness around metric learning. It is intended to be inspirational for productivity rather than serve as a full bibliography.
If you find it useful or like it in some other way, you may want to join our Discord server, where we are running a paper reading club on metric learning.
<p align=center>
<a href="https://discord.gg/tdtYvXjC4h"><img src="https://img.shields.io/badge/Discord-Qdrant-5865F2.svg?logo=discord" alt="Discord"></a>
</p>
## Contributing ๐คฉ
If you want to contribute to this project, but don't know how, you may want to check out the [contributing guide](/CONTRIBUTING.md). It's easy! ๐
## Surveys ๐
<details>
<summary><a href='http://contrib.scikit-learn.org/metric-learn/introduction.html'>What is Metric Learning? </a> - A beginner-friendly starting point for traditional metric learning methods from scikit-learn website.</summary>
> It has proceeding guides for [supervised](http://contrib.scikit-learn.org/metric-learn/supervised.html), [weakly supervised](http://contrib.scikit-learn.org/metric-learn/weakly_supervised.html) and [unsupervised](http://contrib.scikit-learn.org/metric-learn/unsupervised.html) metric learning algorithms in [`metric_learn`](http://contrib.scikit-learn.org/metric-learn/metric_learn.html) package.
</details>
<details>
<summary><a href="https://www.mdpi.com/2073-8994/11/9/1066/htm">Deep Metric Learning: A Survey</a> - A comprehensive
study for newcomers.</summary>
> Factors such as sampling strategies, distance metrics, and network structures are systematically analyzed by comparing the quantitative results of the methods.
</details>
<details>
<summary><a href="https://hav4ik.github.io/articles/deep-metric-learning-survey">Deep Metric Learning: A (Long) Survey</a> - An intuitive survey of the state-of-the-art.</summary>
> It discusses the need for metric learning, old and state-of-the-art approaches, and some real-world use cases.
</details>
<details>
<summary><a href="https://arxiv.org/abs/1812.05944">A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges (with Appendices on Mathematical Background and Detailed Algorithms Explanation)</a> - Intended for those interested in mathematical foundations of metric learning.</summary>
</details>
<details>
<summary><a href="https://arxiv.org/abs/2201.05176">Neural Approaches to Conversational Information Retrieval</a> - A working draft of a 150-page survey book by Microsoft researchers</summary>
</details>
## Applications ๐ฎ
<details>
<summary><a href="https://github.com/openai/CLIP">CLIP</a> - Training a unified vector embedding for image and text. <code>NLP</code> <code>CV</code></summary>
> CLIP offers state-of-the-art zero-shot image classification and image retrieval with a natural language query. See [demo](https://colab.research.google.com/github/openai/clip/blob/master/notebooks/Interacting_with_CLIP.ipynb).
</details>
<details>
<summary><a href="https://github.com/descriptinc/lyrebird-wav2clip">Wav2CLIP</a> - Encoding audio into the same vector space as CLIP. <code>Audio</code> </summary>
> This work achieves zero-shot classification and cross-modal audio retrieval from natural language queries.
</details>
<details>
<summary><a href="https://github.com/facebookresearch/Detic">Detic</a> - Code released for <a href="https://arxiv.org/abs/2201.02605">"Detecting Twenty-thousand Classes using Image-level Supervision"</a>. <code>CV</code></summary>
> It is an open-class object detector to detect any label encoded by CLIP without finetuning. See [demo](https://huggingface.co/spaces/akhaliq/Detic).
</details>
<details>
<summary><a href="https://tfhub.dev/google/collections/gtr/1"Excerpt of 26,649 characters
Read on GitHubWould you bet a product on this? Bounded 0โ100 and slow moving.
matched fp:d4700d54bc8489fa, topic:awesome-list, readme:awesome list, desc:curated list
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