Quiz lié à un guide · Moyen Niveau

Quiz d'apprentissage par transfert

Testez la réutilisation des paramètres pré-entraînés, les fonctionnalités fixes, les taux de réglage fin, la similarité des tâches, les différences de domaine, les fonctionnalités de la couche d'image, la norme par lots et la compilation.

Chemins de guidage associésApprentissage par transfert
Question 1 de 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. Dans l'apprentissage par transfert de type « extraction de fonctionnalités », vous :
    • Geler la plupart des couches et entraîner uniquement une nouvelle couche de sortie
    • Recycler l'ensemble du réseau à un taux d'apprentissage élevé
    • Jetez les poids pré-entraînés
    • N'utilisez aucun réseau neuronal
  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