A Multiple Classifier Approach for Concatenate-Designed Neural Networks

A Multiple Classifier Approach for Concatenate-Designed Neural Networks

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.

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，并且表明该方法不仅比原始模型具有更好的性能，而且收敛速度更快。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。 （阅读更多）