Question

In the context of deep neural networks, which specific normalization technique is mathematically preferred over BatchNorm when training models with small batch sizes to avoid noise injection?

Accepted Answer

The specific normalization technique mathematically preferred over BatchNorm for small batch sizes is Layer Normalization. Batch Normalization works by calculating the mean and variance across the batch dimension, which means its performance relies heavily on having a large number of samples to accurately estimate the distribution of the data. When the batch size is small, these estimates become noisy and unreliable, leading to degraded model performance. Layer Normalization solves this by calculating the mean and variance across the features of a single sample instead of across the batch. Because Layer Normalization computes statistics independently for each individual input, its calculations remain stable regardless of the batch size. This makes it ideal for recurrent neural networks or transformer models where small batch sizes or varying sequence lengths are common. By eliminating the reliance on batch statistics, Layer Normalization avoids the noise injection that occurs when small samples fail to represent the overall population distribution.

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In the context of deep neural networks, which specific normalization technique is mathematically preferred over BatchNorm when training models with small batch sizes to avoid noise injection?