[D] Looking for definition of open-world ish learning problem

Reddit r/MachineLearning / 3/27/2026

💬 OpinionIdeas & Deep Analysis

共有:

Key Points

A model trained with roughly 30 target classes was required at inference to handle a much larger set of classes not fully covered during training.
Instead of standard closed-set classification, the author used metric learning (ArcFace/CosFace-style adaptations) to learn an embedding space with reduced intra-class cosine similarity and increased inter-class distance.
At inference, objects were assigned by computing similarity and clustering above/below a threshold, with the assumption that each cluster corresponds to a target class.
The approach reportedly generalized well to classes the model had not seen during training, prompting the author to ask what this problem setup is called.
The author notes it is “open-world-ish” but not typical OOD detection because everything is clustered rather than labeled as “unknown.”

Hello!

Recently I did a project where I initially had around 30 target classes. But at inference, the model had to be able to handle a lot more classes than these 30 targets i had in my training data. Therefore, I couldn’t just make a ”normal” classifier that predicts one of the 30 target classes.

I instead went with a metric learning approach where i adapted different flavors of arcface/cosface etc. to create an embedding space that tried to maximize inter cosine distance, and minimize intra cosine distance.

At inference, I then set a similarity threshold and clustered objects accordingly. The idea was of course that the objects that formed cluster belonged to the same target class.

It worked surprisingly well on classes the model had never seen before during training.

Now to my question: What is this kind of ML called? Its not really OOD detection since im clustering everything and not really classifying stuff as ”unknown”

submitted by /u/ralfcat
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