引导式测验 · 中等 等级

半监督学习测验

审查混合标签可用性、自训练、一致性、聚类假设、FixMatch、错误强化、图传播和未标记标记。

相关引导路径半监督学习
问题 1 的 8

半监督学习专为以下情况而设计：

预览每个问题8 问题

当您参加测验时，就会出现答案和解释。

  1. 半监督学习专为以下情况而设计：
    • 每个例子都有完整的标签
    • 您根本没有数据
    • 获得标签的成本极其低廉
    • 您有一些标记数据和大量未标记数据
  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 如何结合这两个主要思想？
    • 它对弱增强输入进行伪标记，然后训练强增强版本以匹配
    • 它仅使用标记数据并忽略其他所有内容
    • 它完全删除了增强
    • 它纯粹随机分配标签
  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