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Zestyclose-Check-751 OP t1_ixym3ea wrote

Reply to comment by anonymousTestPoster in [P] Metric learning: theory, practice, code examples by Zestyclose-Check-751

>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.

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anonymousTestPoster t1_ixyvrwy wrote

> Hi, metric learning is an umbrella term like self-supervised learning, detection, and tracking.

This is basically my point, what is the need for an umbrella term? There is an infinitude of ways in which sub topics can be linked together, rather than having:

> people still need some tools and tutorials to solve their problems.

Isn't it better that people appeal towards self-supervised learning, detection, and tracking directly depending on the problem at hand? These sub-topics are sufficiently different that they should be considered quite separately. Even for things like "supervized learning" we consider the sub-problems of regression and classification very differently. Although there is theoretical interest to combine both topics in the discussion of similarities, practically speaking one would choose to take a "classification" or a "regression" task for the specific problem, so that it is ultimately not useful to consider a practical problem as being of "supervized" type, apart from maybe 1-2 sentences in an introduction section of the problem.

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Zestyclose-Check-751 OP t1_ixz32ak wrote

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.

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