在过去半个多世纪中, 随着计算机技术的发展, 神经网络已经在图像、语音、决策等众多领域取得了广泛的应用. 不同学者为了提高神经网络的准确率设计了大量的网络结构, 神经网络也变得越来越复杂和多参数化. 这使得神经网络的训练过程具有很强的非凸性, 相同的网络不同的初始参数往往会训练出不同的模型. 为了更精准地描述两个网络的表现, 前人提出通过统计学方法—随机占优(stochastic dominance)评估不同随机种子对同一网络训练出的不同模型的表现的分布. 本文在此基础上认为, 不同模型在测试集中不同样本上的表现的分布同样值得关注, 并将随机占优方法应用到不同模型在不同样本表现分布的对比中. 通过对图像分割应用中的网络进行实验, 本文关注到不同网络训练出的两个模型其中一个尽管在表现分数上具有一定的优势, 但是其在测试集中不同样本中表现出的离散度可能更强. 实际应用需要表现分数更好同时离散度尽可能小的神经网络模型, 随机占优方法可以有效地对不同模型进行比较从而筛选出更适合的模型.
In the past half-century, with the development of computer technology, neural networks have been widely used in many fields such as images, speeches, and decision-making. To improve the accuracy of neural networks, different scholars have designed a large number of network structures, and thus neural networks have become more and more complex and multi-parametric. As a result, the training process of neural networks has strong non-convexity, and different initial parameters of the same network often train different models. To more accurately describe the performance of two networks, predecessors proposed to evaluate the distribution of the performance of different random seeds on different models trained by the same network through the statistical method of stochastic dominance. On this basis, this study believes that the distribution of the performance of different models on different samples in a test set is also worthy of attention, and thus the stochastic dominance method is applied to compare the distribution of the performance of different models on different samples. Through the experiments on the networks applied in image segmentation, this study finds that for the two models trained by different networks, although one of them has certain advantages in the performance score, it may show stronger dispersion on different samples in the test set. The practical application, however, requires the neural network model with a better performance score and dispersion as small as possible. The stochastic dominance method can effectively compare different models for the selection of a more suitable one.