引导式测验 · 中等 等级
小组标准化测验
测试每个示例的通道组、小批量行为、限制情况、学习的仿射参数以及评估设置的证据。
问题 1 的 8
组标准化在哪些维度上计算统计数据？
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。
- 组标准化在哪些维度上计算统计数据？
- 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
- 对于非常小的批量，为什么 GroupNorm 比 BatchNorm 更好？
- 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
- 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
- GroupNorm 独立于批量大小意味着什么？
- 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
- 哪个 GroupNorm 设置对应于包含所有通道的一组？
- 批量归一化
- 层归一化
- Instance Normalization
- Weight Normalization
- 哪个 GroupNorm 设置对应于每组一个通道？
- 层归一化
- 批量归一化
- RMS Normalization
- Instance Normalization
- 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
- 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