metric learning

Metric learning

The goal of metric learning is to learn a representation function that maps objects into an embedded space. The distance in the embedded space should preserve the objects’ similarity — similar objects get close and dissimilar objects get far away.

In practice, metric learning algorithms ignore the condition of identity of indiscernibles and learn a pseudo-metric.

References

similarity learning

https://paperswithcode.com/task/metric-learning
https://en.wikipedia.org/wiki/Similarity_learning#Metric_learning