Regularization is critical for preventing overfitting and improving generalization in deep neural networks. This guide compares the most common techniques: Dropout, Weight Decay, Batch Normalization, Layer Normalization, and GroupNorm. We explain how each method works, their mathematical foundations, and practical considerations. For computer vision tasks, BatchNorm remains a staple, but GroupNorm offers advantages with small batch sizes. In large language models, LayerNorm is preferred due to its stability across sequence lengths. Dropout and Weight Decay are complementary and often used together. We also discuss recent trends like adaptive regularization and how to combine methods effectively. Whether you're training a CNN or a transformer, understanding these tools is essential for achieving state-of-the-art performance. This guide distills the key insights into actionable advice for your next project.
This post provides a hands-on comparison of Dropout, Weight Decay, BatchNorm, LayerNorm, and GroupNorm, with practical guidance for CV and large language models. It helps practitioners choose the right regularization strategy for their architectures. The topic is evergreen and highly relevant to modern deep learning workflows.