Final answer:
Data augmentation and transfer learning can be used when important data only appears in the validation or test sets.
Step-by-step explanation:
When important data only appears in the validation or test sets, it can be a challenge to obtain that data. In such cases, you can use techniques like data augmentation or transfer learning. Data augmentation involves creating new data by applying transformations to the existing data. Transfer learning allows you to use a pre-trained model on a related task and fine-tune it on your specific task using the available validation or test data.