A crucial requirement for machine learning algorithms is not only to perform well, but also to show robustness and adaptability when encountering novel scenarios. One way to achieve these characteristics is to endow the deep learning models with the ability to detect out-of-distribution (OOD) data, i.e. data that belong to distributions different from the one used during their training. It is even a more complicated situation, when these data usually are multi-label. In this paper, we propose an approach based on evidential deep learning in order to meet these challenges applied to visual recognition problems. More concretely, we designed a CNN architecture that uses a Beta Evidential Neural Network to compute both the likelihood and the predictive uncertainty of the samples. Based on these results, we propose afterwards two new uncertainty-based scores for OOD data detection: (i) OOD - score Max, based on the maximum evidence; and (ii) OOD score - Sum, which considers the evidence from all outputs. Extensive experiments have been carried out to validate the proposed approach using three widely-used datasets: PASCAL-VOC, MS-COCO and NUS-WIDE, demonstrating its outperformance over several State-of-the-Art methods.

翻译：机器学习算法的一个关键要求不仅是性能优异，更需在遇到新场景时表现出鲁棒性与适应性。实现这些特性的一种途径是赋予深度学习模型检测分布外数据的能力，即识别与训练数据分布不同的样本。当这些数据通常具有多标签属性时，情况会变得更加复杂。本文提出一种基于证据深度学习的方法来应对视觉识别任务中的这些挑战。具体而言，我们设计了一种卷积神经网络架构，该架构采用Beta证据神经网络同时计算样本的似然度与预测不确定性。基于这些结果，我们进一步提出两种基于不确定性的新型分布外数据检测评分方法：（i）基于最大证据的OOD-score Max；（ii）考虑所有输出证据的OOD-score Sum。我们使用三个广泛使用的数据集（PASCAL-VOC、MS-COCO和NUS-WIDE）进行了大量实验以验证所提方法，结果表明其性能优于多种现有先进方法。