Normalization Techniques

Group Normalization is a technique to normalize inputs in a neural network by grouping features, improving performance in various tasks.

Instance Normalization adjusts feature maps for each instance separately, enhancing style transfer and image generation tasks.

L1 Normalization is a technique used to scale data by minimizing the absolute sum of the coefficients.

Layer Normalization is a technique used to improve the training of deep learning models by normalizing inputs across features.

A normalized variable adjusts data to a common scale for effective comparison and analysis.