Gradients as a Measure of Uncertainty in Neural Networks

TL;DR

This paper introduces a gradient-based method to measure uncertainty in neural networks, effectively detecting unfamiliar inputs and outperforming existing techniques in out-of-distribution and corrupted data scenarios.

Contribution

The paper proposes using backpropagated gradients as a simple, effective measure of uncertainty in neural networks, improving detection of unfamiliar inputs.

Findings

Outperforms state-of-the-art in out-of-distribution detection by up to 4.8% AUROC

Achieves up to 35.7% improvement in corrupted input detection

Demonstrates gradients as a valuable uncertainty measure in neural networks

Abstract

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.