  Understanding and explaining deep learning models is an imperative task.
Towards this, we propose a method that obtains gradient-based certainty
estimates that also provide visual attention maps. Particularly, we solve for
visual question answering task. We incorporate modern probabilistic deep
learning methods that we further improve by using the gradients for these
estimates. These have two-fold benefits: a) improvement in obtaining the
certainty estimates that correlate better with misclassified samples and b)
improved attention maps that provide state-of-the-art results in terms of
correlation with human attention regions. The improved attention maps result in
consistent improvement for various methods for visual question answering.
Therefore, the proposed technique can be thought of as a recipe for obtaining
improved certainty estimates and explanations for deep learning models. We
provide detailed empirical analysis for the visual question answering task on
all standard benchmarks and comparison with state of the art methods.
