Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization

NeurIPS 2017 Hyeonwoo NohTackgeun YouJonghwan MunBohyung Han

Overfitting is one of the most critical challenges in deep neural networks, and there are various types of regularization methods to improve generalization performance. Injecting noises to hidden units during training, e.g., dropout, is known as a successful regularizer, but it is still not clear enough why such training techniques work well in practice and how we can maximize their benefit in the presence of two conflicting objectives---optimizing to true data distribution and preventing overfitting by regularization... (read more)

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