论文标题
流网络的应用
Applications of the Streaming Networks
论文作者
论文摘要
最近引入了流媒体网络（STNET）作为强大的噪声浪费图像分类的机制。 STNET是一个卷积神经网络的家族，由多个神经网络（流）组成，它们具有不同的输入，其输出被串联并馈入单个联合分类器。原始论文说明了如何从CIFAR10，EUROSAT和UCMERCED数据集中成功地对图像进行分类，当时图像被各种级别的随机零噪声损坏。在本文中，我们证明了STNET能够对被高斯噪声，雾，雪等损坏的图像进行高度准确分类。（CIFAR10损坏的数据集）和低光图像（Carvana DataSet的子集）。我们还引入了一种称为Hybrid Stnets的新型STNET。因此，我们说明当原始训练数据集被噪声或其他转换损坏时，STNET是图像分类的通用工具，这导致了原始图像的信息丢失。
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.
