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A regularization technique that randomly disables neurons during training to prevent overfitting and improve generalization.
Srivastava et al. (2014) introduced dropout: during each training step, a random fraction of units is temporarily removed, forcing the network not to rely on any single neuron. This acts like training a large ensemble of thinned networks and markedly reduces overfitting.
Dropout became a standard ingredient in deep networks and is still used, in various forms, in modern architectures.
Primary, peer-reviewed and archival sources for this definition.
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