Enhancing deep neural network training through learnable adaptive normalization

Jan Benedikt Ruhland

doi:10.1016/j.knosys.2025.113968

Enhancing deep neural network training through learnable adaptive normalization

2025-07-05

0

PRE

AI

DOI：10.1016/j.knosys.2025.113968

Original

Original request for help

Save

Abstract

En 中文

Normalization is a fundamental preprocessing technique in data science, commonly used to standardize data distributions prior to model training. Its primary role is to maintain consistent statistical properties across features, which facilitates efficient learning and enhances training stability. In deep learning, normalization methods are particularly beneficial, as they regulate input distributions within neural networks, promoting more stable training and faster convergence.

Keywords:

Artificial intelligence

Machine learning

Deep learning

AI in Medicine

Enhancing deep neural network training through learnable adaptive normalization

Abstract

Journal

Organization