Virtual Samples for Cloud Classification via Supervised Learning

被引：0

作者：

Liu, Shuang ^{[1
,2
]}

Li, Mei ^{[1
,2
]}

Zhang, Zhong ^{[1
,2
]}

Shi, Mingzhu ^{[1
,2
]}

Cao, Xiaozhong ^{[3
]}

机构：

[1] Tianjin Normal Univ, Tianjin Key Lab Wireless Mobile Commun & Power Tr, Tianjin, Peoples R China

[2] Tianjin Normal Univ, Coll Elect & Commun Engn, Tianjin, Peoples R China

[3] China Meteorol Adm, Meteorol Observat Ctr, Beijing, Peoples R China

来源：

COMMUNICATIONS, SIGNAL PROCESSING, AND SYSTEMS, CSPS 2018, VOL III: SYSTEMS | 2020年 / 517卷

基金：

中国国家自然科学基金;

关键词：

Convolutional neural networks; Generative model; Discriminative model; Cloud classification;

D O I：

10.1007/978-981-13-6508-9_11

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Convolutional neural networks (CNNs) have been widely used in image classification task, which is based on the huge amount of image samples. However, the insufficiency of cloud sample numbers brings obstacles to classify clouds using CNNs. In this paper, we propose to apply Wasserstein generative adversarial network (WGAN) to generate virtual cloud samples via supervised learning. Afterward, we fine-tune a deep CNN model to evaluate the classification performance under different number of virtual cloud samples. The experimental results demonstrate the feasibility of the proposed method.

引用

页码：80 / 86

页数：7

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