{"title":"基于gan的超分辨率深度学习中计算时间与学习精度的权衡","authors":"JooYong Shim, Joongheon Kim, Jong-Kook Kim","doi":"10.1109/ICOIN50884.2021.9333991","DOIUrl":null,"url":null,"abstract":"The trade-off between accuracy and computation should be considered when applying generative adversarial network (GAN)-based image generation to real-world applications. This paper presents a simple yet efficient method based on Progressive Growing of GANs (PGGAN) to exploit the trade-off for image generation. The scheme is evaluated using the LSUN dataset.","PeriodicalId":6741,"journal":{"name":"2021 International Conference on Information Networking (ICOIN)","volume":"1 1","pages":"422-424"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"On the Tradeoff between Computation-Time and Learning-Accuracy in GAN-based Super-Resolution Deep Learning\",\"authors\":\"JooYong Shim, Joongheon Kim, Jong-Kook Kim\",\"doi\":\"10.1109/ICOIN50884.2021.9333991\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The trade-off between accuracy and computation should be considered when applying generative adversarial network (GAN)-based image generation to real-world applications. This paper presents a simple yet efficient method based on Progressive Growing of GANs (PGGAN) to exploit the trade-off for image generation. The scheme is evaluated using the LSUN dataset.\",\"PeriodicalId\":6741,\"journal\":{\"name\":\"2021 International Conference on Information Networking (ICOIN)\",\"volume\":\"1 1\",\"pages\":\"422-424\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Information Networking (ICOIN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICOIN50884.2021.9333991\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Information Networking (ICOIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICOIN50884.2021.9333991","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
On the Tradeoff between Computation-Time and Learning-Accuracy in GAN-based Super-Resolution Deep Learning
The trade-off between accuracy and computation should be considered when applying generative adversarial network (GAN)-based image generation to real-world applications. This paper presents a simple yet efficient method based on Progressive Growing of GANs (PGGAN) to exploit the trade-off for image generation. The scheme is evaluated using the LSUN dataset.
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