ConceptOut-of-Distribution泛化 / 分布外泛化 / OOD
OOD Generalization
A core challenge in machine learning referring to a model's ability to perform inference on new data whose statistical properties differ from those of the training distribution. It serves as an important metric for assessing the real-world robustness of trained models.
Timeline (last 90 days)
Oct 17
Out-of-distribution (OOD) generalization ability reflects the practical value of robots in real deployment more than in-distribution accuracy does.
Unverified50%