Batch Normalization Layer

Batch normalization (BN) layers are a crucial component of deep neural networks, primarily aiming to stabilize training and improve model performance by normalizing the activations of each layer. Current research focuses on leveraging BN layers for improved adaptation in domain adaptation and continual learning scenarios, often by modifying or freezing BN statistics to mitigate catastrophic forgetting or enhance robustness to distribution shifts. This work is significant because effective BN layer management is critical for achieving efficient and accurate model training across diverse applications, including object detection, acoustic scene classification, and medical image analysis.