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Adversarial Dropout for Recurrent Neural Networks
Successful application processing sequential data, such as text and speech,
requires an improved generalization performance of recurrent neural networks
(RNNs). Dropout techniques for RNNs were introduced to respond to these
demands, but we conjecture that the dropout on RNNs could have been improved by
adopting the adversarial concept. This paper investigates ways to improve the
dropout for RNNs by utilizing intentionally generated dropout masks.
Specifically, the guided dropout used in this research is called as adversarial
dropout, which adversarially disconnects neurons that are dominantly used to
predict correct targets over time. Our analysis showed that our regularizer,
which consists of a gap between the original and the reconfigured RNNs, was the
upper bound of the gap between the training and the inference phases of the
random dropout. We demonstrated that minimizing our regularizer improved the
effectiveness of the dropout for RNNs on sequential MNIST tasks,
semi-supervised text classification tasks, and language modeling tasks.Comment: published in AAAI1
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