Autonomous systems use extensively learning-enabled components such as deep neural networks (DNNs) for prediction and decision making. In this paper, we utilize a feedback loop between learning-enabled components used for classification and the sensors of an autonomous system in order to improve the confidence of the predictions. We design a classifier using Inductive Conformal Prediction (ICP) based on a triplet network architecture in order to learn representations that can be used to quantify the similarity between test and training examples. The method allows computing confident set predictions with an error rate predefined using a selected significance level. A feedback loop that queries the sensors for a new input is used to further refine the predictions and increase the classification accuracy. The method is computationally efficient, scalable to high-dimensional inputs, and can be executed in a feedback loop with the system in real-time. The approach is evaluated using a traffic sign recognition dataset and the results show that the error rate is reduced.
翻译:自主系统使用深神经网络(DNNs)等广泛的学习驱动组件进行预测和决策。在本文中,我们利用用于分类的学习驱动组件和自动系统传感器之间的反馈回路,以提高预测的信心。我们根据三重网络结构设计了一个叙级器,使用感应共预报(ICP),以学习可用于量化测试与培训实例相似性的表示方式。该方法允许计算自信设定的预测,并用选定意义水平预先界定出误差率。一个查询传感器进行新输入的反馈回路,用于进一步完善预测,提高分类准确性。该方法在计算上效率,可与高维输入相适应,并可在实时与系统反馈循环中实施。该方法使用交通标志识别数据集进行评估,结果显示错误率降低。