Dropout is a regularization technique used in deep learning models to prevent overfitting. It involves randomly selecting neurons in the network to be ignored or "dropped out" during training, reducing their contribution to the activation of downstream neurons. This helps to prevent complex co-adaptations between neurons, making the model more resilient and less likely to overfit on the training data. During testing or inference, all neurons are activated to make predictions. Dropout has been shown to be effective in improving the performance of deep learning models, particularly with large and complex datasets, and is widely used in practice.
Publication date: