SelfNormalizing
Self-normalizing refers to a property of a processing system in which normalized statistical properties are maintained across successive transformations, reducing the need for external normalization steps. In statistics, this can describe data preprocessing or transformations that aim to standardize features to have zero mean and unit variance, improving the stability and performance of learning algorithms.
In deep learning, the term is most closely associated with self-normalizing neural networks (SNNs). SNNs use
Advantages of self-normalizing networks include reduced reliance on batch normalization and improved training stability, particularly for
Self-normalization contrasts with methods like batch normalization, which explicitly normalize activations using batch statistics during training.