Case context: A training objective combines 5,000 clinic photos with 200,000 web-scraped reference images. The current setting is beta = 1/40, and the team is considering whether another setting would work better.

Question: What does beta = 1/40 do in this setup, and how should the team choose among other beta settings?

Sample answer: With beta = 1/40, the smaller clinic-photo set and the much larger web-image set contribute the same overall amount to the objective, even though one set has far fewer examples. If the team wants a different tradeoff, it should compare other beta values on the development set and select the one that performs best there.

Key points:

• Beta = 1/40 makes the two groups contribute equally in aggregate.
• The 200,000-image set is the auxiliary web-image source.
• Other beta settings can be tried.
• The development set should decide which setting to keep.

Rubric: The response should state the effect of beta = 1/40 and say that candidate beta values are chosen using the development set. It should associate beta with the auxiliary web-image data.