Case context: A team is building a classifier for crop-disease photos taken by handheld phones in the field. It has 4,000 field images from the target setting and 160,000 high-resolution greenhouse images from a separate collection. The team can only afford a modest neural network, so it cannot fully model both sources with equal fidelity.

Question: Explain the capacity issue and decide how the greenhouse images should be weighted during training.

Sample answer: The large greenhouse collection could dominate training and require more model capacity than the team can afford if the system tries to fit both image sources equally well. Because the evaluation data comes from field phone images, the model should focus more on matching that target distribution. The greenhouse images can still be useful, but they should receive a much lower weight so they do not overwhelm the field examples.

Key points:

• The auxiliary greenhouse set is much larger than the target field set
• A small model may not represent both sources equally well
• The field-phone distribution should get higher priority because it matches evaluation
• The greenhouse images should remain in training with lower weight

Rubric: The response should mention limited model capacity, prioritize the field-phone evaluation distribution, and recommend lower—not zero or higher—weight for the greenhouse images.