Question: Explain why having 100,000 synthetic training examples does not prevent overfitting when those examples come from only about 20 car designs. In your answer, address the role of data diversity versus data quantity.

Sample answer: Although 100,000 examples sounds like a large dataset, the true source of information is the underlying variety of car designs, not the raw image count. Because all images derive from only ~20 designs, the model is repeatedly exposed to the same underlying shapes and features with only minor rendering variation. This means the effective diversity of the training set is far smaller than 100,000 would suggest. Since dev/test sets reflect the full real-world distribution of car designs, a model trained on such a narrow set of designs learns features specific to those 20 cars rather than general car-recognition features, causing it to overfit and perform poorly on unseen designs.

Key points:

• 100,000 images is a large quantity but represents only ~20 underlying designs
• Diversity of underlying designs matters more than raw example count
• Dev/test sets reflect the full real-world distribution of car designs
• The mismatch between narrow synthetic diversity and broad real-world diversity causes overfitting

Rubric: Full credit: explains that quantity of images does not equal diversity of underlying designs, notes the mismatch between the narrow training distribution and the broad dev/test distribution, and explains overfitting results from this mismatch. Partial credit: mentions overfitting but does not clearly distinguish quantity from diversity.