针对轻量级模型在光刻热点检测中特征提取能力不足的问题,提出以改进重排网络第2版(shuffle net version 2,ShuffleNetV2)为主干网络,引入多尺度双重注意力(multi-scale dual attention,MSDA)模块,同时融合梯度协调机制(gradient harmon...针对轻量级模型在光刻热点检测中特征提取能力不足的问题,提出以改进重排网络第2版(shuffle net version 2,ShuffleNetV2)为主干网络,引入多尺度双重注意力(multi-scale dual attention,MSDA)模块,同时融合梯度协调机制(gradient harmonizing mechanism,GHM)和基于度量学习思想的加性角度边距(additive angular margin,AAM)的光刻热点检测模型——ShuffleNetV2-MSDA-GHM-AAM(SMGA)。该模型提升了对不同尺度上下文信息的建模与感知能力,优化了特征嵌入空间的类间判别性,缓解了数据集的不平衡。在2012年国际计算机辅助设计会议(2012 international conference on computer-aided design,ICCAD 2012)数据集上进行实验,结果显示,SMGA模型在保持98.22%的较高检测召回率的同时,平均误报数量降低到484个。该模型为实现集成电路设计阶段的高效、低成本光刻热点检测提供了可行方案,具有重要的工程应用价值和推广前景。展开更多
The performance of feature learning for deep convolutional neural networks(DCNNs)is increasing promptly with significant improvement in numerous applications.Recent studies on loss functions clearly describing that be...The performance of feature learning for deep convolutional neural networks(DCNNs)is increasing promptly with significant improvement in numerous applications.Recent studies on loss functions clearly describing that better normalization is helpful for improving the performance of face recognition(FR).Several methods based on different loss functions have been proposed for FR to obtain discriminative features.In this paper,we propose an additive parameter depending on multiplicative angular margin to improve the discriminative power of feature embedding that can be easily implemented.In additive parameter approach,an automatic adjustment of the seedling element as the result of angular marginal seed is offered in a particular way for the angular softmax to learn angularly discriminative features.We train the model on publically available dataset CASIA-WebFace,and our experiments on famous benchmarks YouTube Faces(YTF)and labeled face in the wild(LFW)achieve better performance than the various state-of-the-art approaches.展开更多
文摘针对轻量级模型在光刻热点检测中特征提取能力不足的问题,提出以改进重排网络第2版(shuffle net version 2,ShuffleNetV2)为主干网络,引入多尺度双重注意力(multi-scale dual attention,MSDA)模块,同时融合梯度协调机制(gradient harmonizing mechanism,GHM)和基于度量学习思想的加性角度边距(additive angular margin,AAM)的光刻热点检测模型——ShuffleNetV2-MSDA-GHM-AAM(SMGA)。该模型提升了对不同尺度上下文信息的建模与感知能力,优化了特征嵌入空间的类间判别性,缓解了数据集的不平衡。在2012年国际计算机辅助设计会议(2012 international conference on computer-aided design,ICCAD 2012)数据集上进行实验,结果显示,SMGA模型在保持98.22%的较高检测召回率的同时,平均误报数量降低到484个。该模型为实现集成电路设计阶段的高效、低成本光刻热点检测提供了可行方案,具有重要的工程应用价值和推广前景。
基金The work is supported by the NSF of China(No.11871447)Anhui Initiative in Quantum Information Technologies(AHY150200).
文摘The performance of feature learning for deep convolutional neural networks(DCNNs)is increasing promptly with significant improvement in numerous applications.Recent studies on loss functions clearly describing that better normalization is helpful for improving the performance of face recognition(FR).Several methods based on different loss functions have been proposed for FR to obtain discriminative features.In this paper,we propose an additive parameter depending on multiplicative angular margin to improve the discriminative power of feature embedding that can be easily implemented.In additive parameter approach,an automatic adjustment of the seedling element as the result of angular marginal seed is offered in a particular way for the angular softmax to learn angularly discriminative features.We train the model on publically available dataset CASIA-WebFace,and our experiments on famous benchmarks YouTube Faces(YTF)and labeled face in the wild(LFW)achieve better performance than the various state-of-the-art approaches.
