Abstract
Semantic Embeddings are a popular way to represent knowledge in the field of zero-shot learning. We observe their interpretability and discuss their potential utility in a safety-critical context. Concretely, we propose to use them to add introspection and error detection capabilities to neural network classifiers. First, we show how to create embeddings from symbolic domain knowledge. We discuss how to use them for interpreting mispredictions and propose a simple error detection scheme. We then introduce the concept of semantic distance: a real-valued score that measures confidence in the semantic space. We evaluate this score on a traffic sign classifier and find that it achieves near state-of-the-art performance, while being significantly faster to compute than other confidence scores. Our approach requires no changes to the original network and is thus applicable to any task for which domain knowledge is available.
Abstract (translated)
语义嵌入是零镜头学习领域中常用的知识表示方法。我们观察它们的可解释性，并讨论它们在安全关键环境中的潜在效用。具体地，我们建议使用它们来增加对神经网络分类器的自省和错误检测能力。首先，我们演示如何从符号域知识创建嵌入。我们讨论了如何使用它们来解释预测失误，并提出了一个简单的错误检测方案。然后，我们介绍了语义距离的概念：一个真正的值分数，用来衡量语义空间中的置信度。我们在交通标志分类器上评估这个分数，发现它达到了接近最先进的性能，同时计算速度明显快于其他置信度分数。我们的方法不需要改变原始网络，因此适用于任何领域知识可用的任务。
URL
https://arxiv.org/abs/1905.07733
