Curriculum Dropout

TL;DR

Curriculum Dropout introduces an adaptive dropout schedule inspired by curriculum learning, gradually increasing regularization difficulty during training, which improves neural network generalization across multiple datasets and architectures.

Contribution

The paper proposes a novel adaptive dropout scheduling method that enhances regularization by gradually increasing noise, inspired by curriculum learning principles.

Findings

Improved generalization on seven image classification datasets.

Adaptive scheduling outperforms fixed dropout probabilities.

Matches standard dropout performance in worst cases.

Abstract

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.