A Review on Dropout Regularization Approaches for Deep Neural Networks within the Scholarly Domain

Dropout is one of the most popular regularization methods in the scholarly domain for preventing a neural network model from overfitting in the training phase. Developing an effective dropout regularization technique that complies with the model architecture is crucial in deep learning-related tasks because various neural network architectures have been proposed, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and they have exhibited reasonable performance in their specialized areas. In this paper, we provide a comprehensive and novel review of the state-of-the-art (SOTA) in dropout regularization. We explain various dropout methods, from standard random dropout to AutoDrop…

• NR
  National Research Foundation
• NR
  National Research Foundation of Korea

  Award: NRF-2022R1A2C2012243
• MO
  Ministry of Science and ICT, South Korea