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=== Validation with New Data ===
If new data becomes available, an existing model can be validated by assessing whether the new data is predicted by the old model. If the new data is not predicted by the old model, then the model might not be valid for the researcher's goals.
With this in mind, a modern approach is to validate a neural network is to test its performance on ___domain-shifted data. This ascertains if the model learned ___domain-invariant features. <ref>{{Cite journal |last=Feng |first=Cheng |last2=Zhong |first2=Chaoliang |last3=Wang |first3=Jie |last4=Zhang |first4=Ying |last5=Sun |first5=Jun |last6=Yokota |first6=Yasuto |date=2022-07 |title=Learning Unforgotten Domain-Invariant Representations for Online Unsupervised Domain Adaptation |url=http://dx.doi.org/10.24963/ijcai.2022/410 |journal=Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence |___location=California |publisher=International Joint Conferences on Artificial Intelligence Organization |doi=10.24963/ijcai.2022/410}}</ref>
=== A Note of Caution ===
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