  Dropout has been demonstrated as a simple and effective module to not only
regularize the training process of deep neural networks, but also provide the
uncertainty estimation for prediction. However, the quality of uncertainty
estimation is highly dependent on the dropout probabilities. Most current
models use the same dropout distributions across all data samples due to its
simplicity. Despite the potential gains in the flexibility of modeling
uncertainty, sample-dependent dropout, on the other hand, is less explored as
it often encounters scalability issues or involves non-trivial model changes.
In this paper, we propose contextual dropout with an efficient structural
design as a simple and scalable sample-dependent dropout module, which can be
applied to a wide range of models at the expense of only slightly increased
memory and computational cost. We learn the dropout probabilities with a
variational objective, compatible with both Bernoulli dropout and Gaussian
dropout. We apply the contextual dropout module to various models with
applications to image classification and visual question answering and
demonstrate the scalability of the method with large-scale datasets, such as
ImageNet and VQA 2.0. Our experimental results show that the proposed method
outperforms baseline methods in terms of both accuracy and quality of
uncertainty estimation.
