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Swali 1 ya 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?
    • Kusawazisha Kundi
    • Urekebishaji wa Tabaka
    • Instance Normalization
    • Weight Normalization
  6. Which GroupNorm setting corresponds to one channel per group?
    • Urekebishaji wa Tabaka
    • Kusawazisha Kundi
    • 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