半教師あり学習クイズ

クイズに答えると、答えと解説が表示されます。

1. 半教師あり学習は、次のような状況向けに設計されています。
   • すべての例には完全なラベルが付いています
   • データがまったくありません
   • ラベルは限りなく安価に入手できます
   • ラベル付きのデータが少なく、ラベルのないデータが大量にあります。
2. How does self-training use selected predictions on unlabeled examples?
   • It treats selected model guesses as pseudo-labels for further fitting
   • It converts every guess into a verified human annotation
   • It permanently removes the original labeled examples by definition
   • It chooses labels only from alphabetical order
3. Under consistency regularization, what agreement is encouraged across chosen class-preserving augmentations?
   • A requirement that every arbitrary edit preserve the class
   • Opposite predictions for every view of an example
   • Agreement in the model's predictions
   • Removal of every unlabeled input from the loss
4. What does a cluster assumption propose for grouped observations in a semi-supervised method?
   • Every possible cluster must contain every class equally
   • They are likely to share a label under the assumed data structure
   • All semi-supervised methods guarantee identical clusters
   • Labels are determined by filename order
5. FixMatch は 2 つの主要なアイデアをどのように組み合わせているのでしょうか?
   • 弱く拡張された入力に擬似ラベルを付け、次に一致するように強く拡張されたバージョンをトレーニングします。
   • ラベル付きデータのみを使用し、その他はすべて無視します
   • 拡張を完全に削除します
   • 純粋にランダムにラベルを割り当てます
6. How can confirmation bias arise during pseudo-label training?
   • Only low-confidence labels can ever be wrong
   • Every selected prediction becomes a verified fact
   • Using any verified label prevents the model from learning
   • Incorrect model-generated labels are accepted and then reinforced
7. Which structure distinguishes graph-based label propagation from repeatedly selecting a classifier's confident guesses?
   • Only a threshold on independently predicted class probabilities
   • A similarity graph over labeled and unlabeled observations
   • A list of random output labels with no input similarities
   • A requirement to discard every relationship between observations
8. In the stated scikit-learn semi-supervised interface, which integer marks an unlabeled target entry?
   • Zero, necessarily the first actual class label
   • One, necessarily the second actual class label
   • Minus one
   • Fifty, interpreted as a percentage-confidence threshold