Dropout works by randomly setting a fraction of the input units to 0 at each update during training time, which helps in preventing overfitting by reducing the neural network's reliance on any single neuron. This technique effectively simulates a sparse network with a reduced number of neurons and, as a result, each neural network update is performed with a different "view" of the configured network. Dropout has been particularly effective in training deep neural networks, contributing to significant performance improvements in tasks such as image recognition, natural language processing, and more by enhancing the network's ability to generalize from the training data to unseen data.
Historical overview: The concept of Dropout was introduced by Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov in a 2014 paper. Although the foundational ideas were discussed earlier, Dropout gained widespread popularity following this publication due to its simplicity and effectiveness in improving deep neural network models.
Key contributors: The development of Dropout is most closely associated with Nitish Srivastava and Geoffrey Hinton, among others, at the University of Toronto. Their work played a pivotal role in popularizing Dropout as a crucial technique in training neural networks more effectively.