  Quantifying uncertainty in a model's predictions is important as it enables
the safety of an AI system to be increased by acting on the model's output in
an informed manner. This is crucial for applications where the cost of an error
is high, such as in autonomous vehicle control, medical image analysis,
financial estimations or legal fields. Deep Neural Networks are powerful
predictors that have recently achieved state-of-the-art performance on a wide
spectrum of tasks. Quantifying predictive uncertainty in DNNs is a challenging
and yet on-going problem. In this paper we propose a complete framework to
capture and quantify three known types of uncertainty in DNNs for the task of
image classification. This framework includes an ensemble of CNNs for model
uncertainty, a supervised reconstruction auto-encoder to capture distributional
uncertainty and using the output of activation functions in the last layer of
the network, to capture data uncertainty. Finally we demonstrate the efficiency
of our method on popular image datasets for classification.
