Deep neural networks (DNN) have outstanding performance in various applications. Despite numerous efforts of the research community, out-of-distribution (OOD) samples remain a significant limitation of DNN classifiers. The ability to identify previously unseen inputs as novel is crucial in safety-critical applications such as self-driving cars, unmanned aerial vehicles, and robots. Existing approaches to detect OOD samples treat a DNN as a black box and evaluate the confidence score of the output predictions. Unfortunately, this method frequently fails, because DNNs are not trained to reduce their confidence for OOD inputs. In this work, we introduce a novel method for OOD detection. Our method is motivated by theoretical analysis of neuron activation patterns (NAP) in ReLU-based architectures. The proposed method does not introduce a high computational overhead due to the binary representation of the activation patterns extracted from convolutional layers. The extensive empirical evaluation proves its high performance on various DNN architectures and seven image datasets.

翻译：深度神经网络（DNN）在各类应用中表现出卓越性能。尽管研究界付出了大量努力，分布外（OOD）样本仍是DNN分类器面临的重大局限。在安全关键型应用（如自动驾驶汽车、无人机和机器人）中，识别前所未见输入为新颖样本的能力至关重要。现有的OOD样本检测方法将DNN视为黑箱，并评估输出预测的置信度分数。遗憾的是，该方法常会失效，因为DNN并未被训练为对OOD输入降低置信度。本研究提出一种新颖的OOD检测方法。该方法受基于ReLU架构中神经元激活模式（NAP）理论分析的启发。由于从卷积层提取的激活模式采用二值表示，所提方法不会引入高计算开销。广泛的实证评估证明了该方法在各类DNN架构及七个图像数据集上的优异性能。