Papers › Variational Dropout Sparsifies Deep Neural Networks

Variational Dropout Sparsifies Deep Neural Networks

19 Jan 2017ICML 2017 8arXiv:1701.05369archive 2025-07-28

Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov

We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout. We extend Variational Dropout to the case when dropout rates are unbounded, propose a way to reduce the variance of the gradient estimator and report first experimental results with individual dropout rates per weight. Interestingly, it leads to extremely sparse solutions both in fully-connected and convolutional layers. This effect is similar to automatic relevance determination effect in empirical Bayes but has a number of advantages. We reduce the number of parameters up to 280 times on LeNet architectures and up to 68 times on VGG-like networks with a negligible decrease of accuracy.

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