A. Apply aggressive data augmentation techniques to the training data, including random crops, rotations, and color jittering to simulate the variations in the test data.

B. Fine-tune the model on a small subset of the test data to adapt to the specific characteristics of the test distribution.

C. Use a domain adaptation technique such as Domain Adversarial Neural Networks (DANN) to learn domain-invariant features.

D. Reduce the size of the model to prevent overfitting to the training data.

E. Only evaluate on a subset of the test data that closely resembles the training data.