一种自学习不确定度的神经网络架构

An Neural Network Framework of Self-Learning Uncertainty

在自动驾驶这类多传感器融合任务中,每个子任务的不确定度对数据融合的策略和结果都有着至关重要的影响,为使整体系统能够在多工况下稳定运行,必须要求计算模型以较低的不确定度运行。现有方法仅能在神经网络预测过程中求得不确定度,很少有方法能够通过自学习的方式降低模型的不确定度。为解决上述问题,提出了不确定度学习层和不确定度损失项等概念,设计了一种能够通过自学习的方式降低不确定度的神经网络架构(ULNN),从而增强神经网络模型预测的稳健性。在CIFAR-10和CIFAR-100数据集上的实验表明,ULNN能够有效地降低模型不确定度,在两个数据集上分别降低了26倍和12倍的不确定度。进一步在CamVid数据集上的语义分割实验中证明了ULNN的通用性。

In the multi-sensor fusion tasks of automatic drive,the strategy and the results of the data fusion are greatly influenced by the uncertainty of each subtask.To keep the whole system run steadily in multiple circumstances,the calculation model must operate with low uncertainty.The existing methods can only obtain uncertainty in the neural network prediction process,and few methods can reduce the uncertainty of the model in a self-learning method.To address the above problems,the concepts of uncertainty learning layer and uncertainty loss term are proposed,and a neural network architecture（ULNN）which can reduce uncertainty by self-learning method is designed to enhance the robustness of neural network model prediction.Experiments on CIFAR-10 and CIFAR-100 datasets show that ULNN can effectively reduce the model uncertainty and obtain 26 and 12 times lower uncertainty on the two data sets respectively.The universality of ULNN is proved by the experimental results of semantic segmentation on CamVid dataset.