流媒体网络的应用
Applications of the Streaming Networks
摘要：
最近，流网络（STnets）已被引入作为一种机制，强大的噪声损坏的图像分类。STnets是一个卷积神经网络家族，它由多个神经网络（流）组成，这些网络具有不同的输入，它们的输出被连接并馈送到单个联合分类器中。原始论文说明了STnets如何成功地对来自Cifar10，EuroSat和UCmerced数据集的图像进行分类，当图像被各种级别的随机零噪声破坏时。在本文中，我们证明了STnets能够对高斯噪声，雾，雪等（Cifar10损坏数据集）和低光图像（Carnival数据集的子集）损坏的图像进行高精度分类。我们还介绍了一种新型的STnets称为混合STnets。因此，我们说明，STnets是一个通用的图像分类的工具，当原始训练数据集被噪声或其他变换损坏，从而导致从原始图像的信息丢失。
英文摘要：
Most recently Streaming Networks (STnets) have been introduced as a mechanism of robust noise-corrupted images classification. STnets is a family of convolutional neural networks, which consists of multiple neural networks (streams), which have different inputs and their outputs are concatenated and fed into a single joint classifier. The original paper has illustrated how STnets can successfully classify images from Cifar10, EuroSat and UCmerced datasets, when images were corrupted with various levels of random zero noise. In this paper, we demonstrate that STnets are capable of high accuracy classification of images corrupted with Gaussian noise, fog, snow, etc. (Cifar10 corrupted dataset) and low light images (subset of Carvana dataset). We also introduce a new type of STnets called Hybrid STnets. Thus, we illustrate that STnets is a universal tool of image classification when original training dataset is corrupted with noise or other transformations, which lead to information loss from original images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
