Few-shot Learning algorithms can be effectively applied to fields where certain categories have only a small amount of data or a small amount of labeled data,such as medical images,terrorist surveillance,and so on.The...Few-shot Learning algorithms can be effectively applied to fields where certain categories have only a small amount of data or a small amount of labeled data,such as medical images,terrorist surveillance,and so on.The Metric Learning in the Few-shot Learning algorithmis classified by measuring the similarity between the classified samples and the unclassified samples.This paper improves the Prototypical Network in the Metric Learning,and changes its core metric function to Manhattan distance.The Convolutional Neural Network of the embedded module is changed,and mechanisms such as average pooling and Dropout are added.Through comparative experiments,it is found that thismodel can converge in a small number of iterations(below 15,000 episodes),and its performance exceeds algorithms such asMAML.Research shows that replacingManhattan distance with Euclidean distance can effectively improve the classification effect of the Prototypical Network,and mechanisms such as average pooling and Dropout can also effectively improve the model.展开更多
A novel framework for non-Manhattan channel routing considering crosstalk and wire length reduction is proposed. To handle crosstalk and wire length reduction problem, an improved non-Manhattan router in the gridded r...A novel framework for non-Manhattan channel routing considering crosstalk and wire length reduction is proposed. To handle crosstalk and wire length reduction problem, an improved non-Manhattan router in the gridded routing model is developed, and a novel rerouting algorithm based on segment-rerouting technique and layer reassignment is presented. Simulation exDeriments demonstrate that our algorithm achieves verv good results.展开更多
文摘Few-shot Learning algorithms can be effectively applied to fields where certain categories have only a small amount of data or a small amount of labeled data,such as medical images,terrorist surveillance,and so on.The Metric Learning in the Few-shot Learning algorithmis classified by measuring the similarity between the classified samples and the unclassified samples.This paper improves the Prototypical Network in the Metric Learning,and changes its core metric function to Manhattan distance.The Convolutional Neural Network of the embedded module is changed,and mechanisms such as average pooling and Dropout are added.Through comparative experiments,it is found that thismodel can converge in a small number of iterations(below 15,000 episodes),and its performance exceeds algorithms such asMAML.Research shows that replacingManhattan distance with Euclidean distance can effectively improve the classification effect of the Prototypical Network,and mechanisms such as average pooling and Dropout can also effectively improve the model.
文摘A novel framework for non-Manhattan channel routing considering crosstalk and wire length reduction is proposed. To handle crosstalk and wire length reduction problem, an improved non-Manhattan router in the gridded routing model is developed, and a novel rerouting algorithm based on segment-rerouting technique and layer reassignment is presented. Simulation exDeriments demonstrate that our algorithm achieves verv good results.
