AutoDropout: Learning Dropout Patterns to Regularize Deep Networks
As an improvement over existing Dropout regularization variants for deep neural networks (e.g. regular Dropout, SpatialDropout, DropBlock) that have a randomized structure with certain fixed parameters, the authors develop a reinforcement learning approach for finding better Dropout patterns for various network architectures.