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A Multiple Classifier Approach for Concatenate-Designed Neural Networks
arXiv - CS - Neural and Evolutionary Computing Pub Date : 2021-01-14 , DOI: arxiv-2101.05457 Ka-Hou Chan, Sio-Kei Im, Wei Ke
arXiv - CS - Neural and Evolutionary Computing Pub Date : 2021-01-14 , DOI: arxiv-2101.05457 Ka-Hou Chan, Sio-Kei Im, Wei Ke
This article introduces a multiple classifier method to improve the
performance of concatenate-designed neural networks, such as ResNet and
DenseNet, with the purpose to alleviate the pressure on the final classifier.
We give the design of the classifiers, which collects the features produced
between the network sets, and present the constituent layers and the activation
function for the classifiers, to calculate the classification score of each
classifier. We use the L2 normalization method to obtain the classifier score
instead of the Softmax normalization. We also determine the conditions that can
enhance convergence. As a result, the proposed classifiers are able to improve
the accuracy in the experimental cases significantly, and show that the method
not only has better performance than the original models, but also produces
faster convergence. Moreover, our classifiers are general and can be applied to
all classification related concatenate-designed network models.
中文翻译:
串联设计神经网络的多分类器方法
本文介绍了一种多分类器方法,以改善串联设计的神经网络(如ResNet和DenseNet)的性能,目的是减轻最终分类器的压力。我们给出分类器的设计,该分类器收集网络集之间产生的特征,并给出分类器的组成层和激活函数,以计算每个分类器的分类分数。我们使用L2归一化方法来获得分类器得分,而不是Softmax归一化。我们还确定了可以增强融合的条件。结果,提出的分类器能够在实验情况下显着提高准确性,并表明该方法不仅具有比原始模型更好的性能,但也会产生更快的收敛。而且,我们的分类器是通用的,可以应用于所有与分类相关的串联设计网络模型。
更新日期:2021-01-15
中文翻译:
串联设计神经网络的多分类器方法
本文介绍了一种多分类器方法,以改善串联设计的神经网络(如ResNet和DenseNet)的性能,目的是减轻最终分类器的压力。我们给出分类器的设计,该分类器收集网络集之间产生的特征,并给出分类器的组成层和激活函数,以计算每个分类器的分类分数。我们使用L2归一化方法来获得分类器得分,而不是Softmax归一化。我们还确定了可以增强融合的条件。结果,提出的分类器能够在实验情况下显着提高准确性,并表明该方法不仅具有比原始模型更好的性能,但也会产生更快的收敛。而且,我们的分类器是通用的,可以应用于所有与分类相关的串联设计网络模型。