论文标题
梯度作为神经网络不确定性的量度
Gradients as a Measure of Uncertainty in Neural Networks
论文作者
论文摘要
尽管现代神经网络取得了巨大的成功，但即使模型遇到不熟悉的条件的输入，它们也会过分自信。检测这种输入对于防止模型做出可能危害神经网络现实世界应用的天真预测至关重要。在本文中，我们解决了一个具有挑战性的问题，即设计简单而有效的深度神经网络不确定性。具体而言，我们建议利用反向传播的梯度来量化训练有素的模型的不确定性。渐变描述了模型正确表示给定输入所需的更改量，从而提供了对模型在输入方面的熟悉程度和确定性的宝贵见解。我们证明了梯度作为模型不确定性在检测不熟悉输入的应用中的有效性，包括分布式和损坏的样本。我们表明，我们基于梯度的方法在分布外检测中优于最先进的方法，高达AUROC得分的4.8％，在损坏的输入检测中最多比最高的方法。
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.
