  Dropout is a very effective way of regularizing neural networks.
Stochastically "dropping out" units with a certain probability discourages
over-specific co-adaptations of feature detectors, preventing overfitting and
improving network generalization. Besides, Dropout can be interpreted as an
approximate model aggregation technique, where an exponential number of smaller
networks are averaged in order to get a more powerful ensemble. In this paper,
we show that using a fixed dropout probability during training is a suboptimal
choice. We thus propose a time scheduling for the probability of retaining
neurons in the network. This induces an adaptive regularization scheme that
smoothly increases the difficulty of the optimization problem. This idea of
"starting easy" and adaptively increasing the difficulty of the learning
problem has its roots in curriculum learning and allows one to train better
models. Indeed, we prove that our optimization strategy implements a very
general curriculum scheme, by gradually adding noise to both the input and
intermediate feature representations within the network architecture.
Experiments on seven image classification datasets and different network
architectures show that our method, named Curriculum Dropout, frequently yields
to better generalization and, at worst, performs just as well as the standard
Dropout method.
