Metric Learning for Anomaly Detection Anomaly detection is on | Neural Networks Engineering

Metric Learning for Anomaly Detection

Anomaly detection is one of those tasks to which it is challenging to apply classical ML methods directly.
The balancing of normal and abnormal examples and the internal inconsistency of anomalies make classifier training a challenging task.

And the difficulty is often related to data labeling, which in the case of anomalies may not be trivial.

The metric learning approach avoids the explicit separation into classes while combining the advantage of modeling the subject domain with the knowledge of specific anomalous examples.

In our case study, we are solving the problem of estimating the quality of coffee beans and determining the type of defects.

We trained Auto-Encoder on unlabeled samples and made fine-tuning on a small fraction of labeled ones.
This approach achieves results equivalent to conventional classification but requires orders of magnitude less labeled data.