Batch Normalization

Batch Normalization (BatchNorm) is a neural networkNeural NetworkA Neural Network is a computing system inspired by the structure of biological neural networks in the brain. It forms... technique that normalizes the activations of each layer by adjusting and scaling them based on the mean and variance computed over the current mini-batch of training data. For each feature channel, BatchNorm subtracts the batch mean and divides by the batch standard deviation, then applies learned scale and shift parameters that allow the network to recover the optimal distribution for each layer. This normalization reduces internal covariate shift, where the distribution of layer inputs changes as preceding layers are updated during training. Benefits for Deep Geospatial Model TrainingBatch Normalization enables faster and more stable training of deep CNNs and other architectures used in geospatial AI. It allows higher learning rates without risking divergence, accelerating convergence. It reduces sensitivity to weight initialization, making model training more robust. BatchNorm acts as a form of regularizationRegularizationRegularization encompasses techniques that prevent machine learning models from overfitting to training data, ensurin..., sometimes reducing the need for dropoutDropoutDropout is a regularization technique that randomly deactivates neurons during training, preventing neural networks f.... For geospatial models processing satellite imagerySatellite ImagerySatellite imagery consists of photographs and data captured by Earth observation satellites orbiting the planet. Thes... with varying atmospheric conditions and illumination across scenes, BatchNorm helps the network adapt to this input variability. It is a standard component in architectures like ResNetResNetResNet (Residual Network) is a deep neural network architecture that uses skip connections to enable training of very..., U-NetU-NetU-Net is an encoder-decoder neural network architecture with skip connections designed for precise image segmentation..., and Feature Pyramid Networks that form the backbone of most geospatial computer visionComputer VisionComputer Vision is a field of artificial intelligence that enables machines to interpret and understand visual inform... systems. Variants and Practical ConsiderationsGroup Normalization and Layer Normalization offer alternatives when batch sizes must be small due to memory constraints from processing large satellite image tiles. Instance Normalization, used in style transfer applications, normalizes each sample independently. During inference, BatchNorm uses running statistics accumulated during training rather than batch statistics, ensuring consistent predictions for single images. The interaction between BatchNorm and other training techniques like mixed-precision training and distributed training requires attention in large-scale geospatial model development.