Novelty detection determines whether a new example differs significantly from the distribution represented in the training data. It is commonly used to identify inputs that may be unfamiliar, anomalous, or unsuitable for ordinary model assumptions.
The process of determining whether a new (novel) example comes from the same distribution as the training set. In other words, after training on the training set, novelty detection determines whether a new example (during inference or during additional training) is an outlier. Contrast with outlier detection.