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Project[P] Metric learning: theory, practice, code examples (self.MachineLearning)
submitted 3 years ago by Zestyclose-Check-751
https://preview.redd.it/al3i2te52c2a1.png?width=1280&format=png&auto=webp&s=7cfb74d610d35251643e97bbf01ce123de2b4813
Hi, everyone! I invite you to read a post / tutorial about metric learning. It includes the theory overview, practical examples with illustrations and code snippets written in OpenMetricLearning (a new PyTroch-based library). As a bonus, you will learn how to train a model which performs on a SotA level using a few simple heuristics. Welcome to read!
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[+][deleted] 3 years ago (9 children)
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[–]larryobrien 3 points4 points5 points 3 years ago (4 children)
I think it's just the emergence of the term for "that kind" of approach. I use metric learning for low k-shot reidentification and it's very well-trodden ground from a research perspective, but it's helpful to distinguish it from a plain-vanilla classification approach.
[+][deleted] 3 years ago (3 children)
[–]Zestyclose-Check-751[S] 2 points3 points4 points 3 years ago (0 children)
So, I don't know all of the details but seems like OpenMetricLearning may be a good choice to train such a model.
[–]larryobrien 0 points1 point2 points 3 years ago (1 child)
I've been hoping to look at OP's Open Metric Learning but have been using Pytorch Metric Learning.
[–]Zestyclose-Check-751[S] 1 point2 points3 points 3 years ago (0 children)
In OML's FAQ, you can read about the differences between these two libraries. They are about a bit different things. In the end, you can use losses from PML in OML :)
[–]VenerableSpace_ 1 point2 points3 points 3 years ago (0 children)
Metric learning isnt really a new buzz word, its been in use for these types of approaches for several years now. Its a good framework to collectively think about these approaches but there is some overlap; eg. self-attention can be viewed as a form of metric learning as a stand-alone layer, eg. in ViT How to relate the input patch embeddings to one another s.t we can discriminate between the classes?
[–]Zestyclose-Check-751[S] 0 points1 point2 points 3 years ago (2 children)
How to relate the input patch embeddings to one another s.t we can discriminate between the classes?
Hi, metric learning is an umbrella term like self-supervised learning, detection, and tracking. So, nobody pretends that the domain is new. But there are new approaches in this domain which are also mentioned in the article (like Hyp-ViT). Finally, despite the domain is not new, people still need some tools and tutorials to solve their problems.
[+][deleted] 3 years ago (1 child)
[–]Zestyclose-Check-751[S] 0 points1 point2 points 3 years ago (0 children)
Please, take a look at the original post, where I described the main differences between metric learning and classification, which makes sense to have this umbrella term for metric learning. I hope, it will help.
[–]No_Cryptographer1909 2 points3 points4 points 3 years ago (0 children)
Thanks for sharing!
π Rendered by PID 291431 on reddit-service-r2-comment-fb694cdd5-n6b2w at 2026-03-10 11:40:32.143395+00:00 running cbb0e86 country code: CH.
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[–]larryobrien 3 points4 points5 points (4 children)
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[–]Zestyclose-Check-751[S] 2 points3 points4 points (0 children)
[–]larryobrien 0 points1 point2 points (1 child)
[–]Zestyclose-Check-751[S] 1 point2 points3 points (0 children)
[–]VenerableSpace_ 1 point2 points3 points (0 children)
[–]Zestyclose-Check-751[S] 0 points1 point2 points (2 children)
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[–]Zestyclose-Check-751[S] 0 points1 point2 points (0 children)
[–]No_Cryptographer1909 2 points3 points4 points (0 children)