  Despite tremendous success of modern neural networks, they are known to be
overconfident even when the model encounters inputs with unfamiliar conditions.
Detecting such inputs is vital to preventing models from making naive
predictions that may jeopardize real-world applications of neural networks. In
this paper, we address the challenging problem of devising a simple yet
effective measure of uncertainty in deep neural networks. Specifically, we
propose to utilize backpropagated gradients to quantify the uncertainty of
trained models. Gradients depict the required amount of change for a model to
properly represent given inputs, thus providing a valuable insight into how
familiar and certain the model is regarding the inputs. We demonstrate the
effectiveness of gradients as a measure of model uncertainty in applications of
detecting unfamiliar inputs, including out-of-distribution and corrupted
samples. We show that our gradient-based method outperforms state-of-the-art
methods by up to 4.8% of AUROC score in out-of-distribution detection and 35.7%
in corrupted input detection.
