Context normalization: A new approach for the stability and improvement of neural network performance
Bilal Faye, Hanane Azzag, Mustapha Lebbah, Fangchen Feng · Data & Knowledge Engineering · 2024
Deep neural networks face challenges with distribution shifts across layers, affecting model convergence and performance. While Batch Normalization (BN) addresses these issues, its reliance on a single Gaussian distribution assumption limits adaptability. To overcome this, alternatives like Layer Normalization, Group Normalization, and Mixture Normalization emerged, yet struggle with dynamic activation distributions. We propose ”Context Normalization” (CN), introducing contexts constructed from domain knowledge. CN normalizes data within the same context, enabling local representation. During backpropagation, CN learns normalized parameters and model weights for each context, ensuring efficient convergence and superior performance compared to BN and MN. This approach emphasizes context utilization, offering a fresh perspective on activation normalization in neural networks. We release our code at https://github.com/b-faye/Context-Normalization .