Label noise

Errors or inconsistencies in the labels associated with data.

Noise may be sporadic or systematic, such as a repeated misunderstanding of an action boundary. Review should identify both the affected items and the underlying rule or process failure. Automatically generated labels can contain noise too, even when they are internally consistent.

In practice

Every failed placement after an initial lift is mislabeled successful because the annotation process ignores the final outcome.