Dropout Decoded: Comprehensive Classification and Analysis of Neural Network Regularization Methods

Neural networks are powerful tools for pattern recognition but are often prone to overfitting, limiting their real-world effectiveness. This paper surveys dropout and its numerous variants—regularization techniques that enable neural networks to generalize more effectively to unseen data. A new classification framework is proposed that organizes 50 dropout methods based on their performance across three data categories: sequential, image-based, and general datasets. This structure provides insight into the optimal application of each method. Additionally, the paper examines dropout’s role in improving performance through increased sparsity, enhanced inter-layer interactions, and reduced neuron dependency. The findings establish a foundation for further exploration of regularization strategies and assist researchers and practitioners in developing more robust and reliable neural network architectures.