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What is the difference between BatchNorm and LayerNorm in training stability?
Updated May 17, 2026
Short answer
BatchNorm normalizes across batch dimension; LayerNorm normalizes across features per sample.
Deep explanation
BatchNorm depends on batch statistics and is sensitive to batch size, making it unstable for small batches. LayerNorm normalizes each sample independently, making it stable for transformers and sequence models.
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