A pretrained visual encoder is adapted using a small labeled dataset. What is a reasonable initial strategy?

1. Discard every learned weight without comparison
2. Train and evaluate on exactly the same examples
3. Evaluate transfer learning before training a large model from scratch
4. Choose the largest architecture solely by parameter count