[D] Is this considered unsupervised or semi-supervised learning in anomaly detection?

Reddit r/MachineLearning / 4/7/2026

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Key Points

The setup trains an anomaly detector using only benign/normal data, focusing on modeling normal behavior without class labels during training (often described as one-class or unsupervised representation learning).
At evaluation time, the decision threshold is selected using labeled validation data to maximize F1-score, which introduces supervised elements only for calibration rather than for learning the representation.
The core ambiguity is whether threshold tuning with labels should reclassify the overall approach as semi-supervised, given that training uses no labels.
The question seeks appropriate terminology for describing the method in a paper without overclaiming, suggesting a careful distinction between unsupervised (or one-class) training and supervised threshold calibration.
A practical way to describe such work is to explicitly state “unsupervised/one-class anomaly detection with labeled validation for threshold calibration,” rather than claiming full semi-supervised learning if representation learning remains label-free.

Hi 👋🏼, I’m working on an anomaly detection setup and I’m a bit unsure how to correctly describe it from a learning perspective.

The model is trained using only one class of data (normal/benign), without using any labels during training. In other words, the learning phase is based entirely on modelling normal behaviour rather than distinguishing between classes.

At evaluation time, I select a decision threshold on a validation set by choosing the value that maximizes the F1-score.

So the representation learning itself is unsupervised (or one-class), but the final decision boundary is chosen using labeled validation data.

I’ve seen different terminology used for similar setups. Some sources refer to this as semi-supervised, while others describe it as unsupervised anomaly detection with threshold calibration.

What would be the most accurate way to describe this setting in a paper without overclaiming?

submitted by /u/Opening-Rich-4425
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