การทำให้เป็นมาตรฐานแบบแบตช์และการทำให้เป็นมาตรฐานของเลเยอร์

Activation normalization: why the network is unstable without it

Even with proper Xavier/He init, activation variance drifts during training. Weights change, distributions shift — this is called internal covariate shift. Each layer gets a progressively “worse” input and must keep adapting. Normalization layers fix this directly: they force activations toward a target distribution after each layer.

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