引导式测验 · 中等 等级

迁移学习测验

测试预训练的参数重用、固定特征、微调率、任务相似性、领域差异、图像层特征、批量归一化和编译。

相关引导路径迁移学习
问题 1 的 8

Which learned starting point can transfer learning reuse for a target task?

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  1. Which learned starting point can transfer learning reuse for a target task?
    • Only the source dataset filename
    • A requirement to erase all learned parameters
    • A guarantee that the original output labels are the new labels
    • Pretrained parameters or representations
  2. 在“特征提取”风格的迁移学习中，您通常：
    • 冻结大多数层并仅训练新的输出层
    • 以高学习率重新训练整个网络
    • 扔掉预先训练的权重
    • 根本不使用神经网络
  3. Why does the illustrated fine-tuning workflow recommend a small learning rate for pretrained weights?
    • To make the pretrained weights mathematically unable to change
    • To guarantee that every target example is memorized
    • To limit disruptive changes to the existing representation
    • To replace the need for a target loss
  4. Which target setup can make fixed feature extraction worth comparing with other adaptation strategies?
    • A task defined only by unrelated output filenames
    • Limited target data with a task related to the source task
    • A requirement to discard every pretrained representation
    • A guarantee that the source task and target task must have identical labels
  5. What concern arises when the target input distribution differs from the source pretraining distribution?
    • The reused representation may fit the target task poorly
    • The model necessarily stops containing parameters
    • All target examples become source training examples
    • Every evaluation score must improve
  6. What did the cited image-network transfer study distinguish about features across layers?
    • Every early feature transfers perfectly to every possible task
    • Later layers contain only random unused numbers
    • All network layers necessarily encode identical features
    • Earlier features were often more general and later features more source-task specialized in its setup
  7. In the documented Keras fine-tuning workflow, why keep base-model BatchNormalization layers in inference mode?
    • To make all pretrained weights update at the same rate
    • To avoid disrupting their stored moving mean and variance statistics
    • To replace the target task's output head
    • To force new moving statistics from every target batch regardless of the workflow
  8. After changing layer trainable settings in the stated Keras workflow, what refreshes the compiled training configuration?
    • Changing only an output filename
    • Resetting all pretrained parameters to random values
    • Recompiling the model
    • Keeping the old compiled configuration without modification