【摘要】 针对智慧交通场景中样本较少且难以获取的问题,提出一种张量计算与小样本学习相结合的综合模型,从而应对目标域样本不足导致训练效果差的情况。构建基于张量计算的多维计算模型,处理智慧交通场景中的多维异构数据,基于数据的时空相关性获得融合数据张量,将融合数据作为输入数据,经由小样本学习模型进行训练,最终根据消融实验结果比较分析基于不同张量计算方案和小样本学习方法的张量小样本学习模型性能。仿真结果表明,相较于2种基于度量的小样本学习模型:原型网络和匹配网络,基于元学习的小样本学习模型和张量计算模型相结合后的可信度更高,并且基于不同的张量融合方案,元学习模型的准确率和F1值得到了不同程度的提升,其中基于逆分解张量融合方案的模型准确率可达0.95,性能优于平行因子分解(CPD)融合方案。更多还原

【Abstract】 A comprehensive model combining tensor calculation and few-shot learning is proposed to address the problem of limited and difficult-to-obtain samples in intelligent transportation scenarios such that the issue of unsatisfactory training effect caused by insufficient samples in the target domain can be solved.A multi-dimensional computing model is constructed based on tensor calculation,multi-dimensional heterogeneous data in intelligent transportation scenarios are processed,fused data tensors are obtained based on the spatio-temporal correlation of the data,fused data are used as input data,training is performed using few-shot learning models,and the performances of tensor few-shot learning models based on different tensor calculation schemes and ablation experimental results are compared and analyzed.Simulation results show that compared with two metric-based few-shot learning models, i.e.,the prototype network and matching network,the combination of a meta-learning-based few-shot learning model and a tensor calculation model presents higher credibility.Moreover,by adopting different tensor fusion schemes,the accuracy and F1 values of the meta-learning model improved to varying degrees.The model based on the inverse-decomposition tensor-fusion scheme offers a maximum accuracy of 0.95, which renders it superior to the CANDECOMP/PARAFAC Decomposition(CPD) fusion scheme in terms of performance.更多还原