Quiz lié à un guide · Moyen Niveau

Quiz sur la normalisation des groupes

Teste les groupes de canaux par exemple, le comportement des petits lots, les cas limites, les paramètres affines appris et les preuves issues des paramètres d'évaluation.

Chemins de guidage associésNormalisation de groupe
Question 1 de 8

Across what dimensions does Group Normalization compute statistics?

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  1. Across what dimensions does Group Normalization compute statistics?
    • Only across examples in the batch
    • Across all model layers
    • Across the training and validation sets together
    • Spatial dimensions and channels within a group for each sample
  2. Why can GroupNorm be preferable to BatchNorm for very small batches?
    • It uses the batch mean more accurately with one sample
    • Its statistics are independent of batch size
    • It removes all learned scale parameters
    • It requires a larger batch
  3. How does GroupNorm divide feature channels?
    • Into groups, then normalizes each group’s values within each example
    • Into classes across the dataset
    • Into separate networks for each batch member
    • Into groups based on labels only
  4. What does GroupNorm’s independence from batch size mean?
    • Its output is identical for all batch sizes and all models
    • It eliminates the need for training batches
    • Its per-example normalization does not use other examples’ statistics
    • It guarantees the same accuracy on every task
  5. Which GroupNorm setting corresponds to one group containing all channels?
    • Normalisation par lots
    • Normalisation des couches
    • Instance Normalization
    • Weight Normalization
  6. Which GroupNorm setting corresponds to one channel per group?
    • Normalisation des couches
    • Normalisation par lots
    • RMS Normalization
    • Instance Normalization
  7. What does each channel’s learned scale and shift do after normalization?
    • Sets the group membership from labels
    • Computes batch statistics
    • Allows the network to adjust the normalized feature values
    • Removes the normalized activations
  8. What is a careful interpretation of GroupNorm’s reported results?
    • It performed well in tested small-batch vision settings; task-specific evaluation remains needed
    • It replaces every normalization method in all networks
    • It guarantees identical results for every batch size and task
    • It removes the need to tune group count