Adapt a model trained on general images to medical images.
Explain reuse and its limitations.
Choose two correct statements.
Transfer learning reuses representations from another domain, but target-domain distribution, labels, and negative transfer must be evaluated.
Detailed explanation
It can reduce the target-data requirement.
It can reduce the target-data requirement.
The source model may not generalize or may cause negative transfer.
The source model may not generalize or may cause negative transfer.
Domain differences still matter.
Domain differences still matter.
The target task still requires validation.
The target task still requires validation.
Try it yourself
An example you can run in a temporary verification environment.
AWS公式AIF-C01 Domain 2.1の基盤モデル、転移学習、適応を確認する。Expected result
転移学習の利点と、対象ドメイン評価の必要性を説明できる。Key points
- Reuse
- Domain shift
- Negative transfer
Notes
- Environment: AWS公式AIF-C01試験ガイドとAWS公式ドキュメントの確認
- Command output formatting can vary slightly by distribution or tool version.
- Run the example in a temporary directory or process when possible.
Foundation review
Read the scope first
Check whether the command acts on the current shell, a new process, an existing process, or a file.
Verify the observable result
Use the supplied command and compare the output with the expected result.