论文标题
检测到神经网络的异常输入
Detecting unusual input to neural networks
论文作者
论文摘要
在输入上评估神经网络与训练数据明显不同可能会导致预测不稳定。我们研究一种方法，可以通过评估其信息内容与学习参数相比评估其信息内容的异常性。该技术可用于判断网络是否适合处理某个输入，并提出一个危险信号，即意外行为可能存在。我们比较了各种方法对各种数据集和方案的文献进行不确定性评估的方法。具体而言，我们引入了一种简单，有效的方法，该方法允许直接比较单个输入点的此类指标的输出，即使这些指标生存在不同的尺度上。
Evaluating a neural network on an input that differs markedly from the training data might cause erratic and flawed predictions. We study a method that judges the unusualness of an input by evaluating its informative content compared to the learned parameters. This technique can be used to judge whether a network is suitable for processing a certain input and to raise a red flag that unexpected behavior might lie ahead. We compare our approach to various methods for uncertainty evaluation from the literature for various datasets and scenarios. Specifically, we introduce a simple, effective method that allows to directly compare the output of such metrics for single input points even if these metrics live on different scales.
