{"title":"开放神经图像生成器","authors":"Andrei-Marius Avram, Luciana Morogan, Stefan-Adrian Toma","doi":"10.1109/COMM48946.2020.9142009","DOIUrl":null,"url":null,"abstract":"Generative models are statistical models that learn a true underlying data distribution from samples using unsupervised learning, aiming to generate new data points with some variation. In this paper, we introduce OpenNIG (Open Neural Image Generator), an open-source neural networks toolkit for image generation. It offers the possibility to easily train, validate and test state of the art models. The framework also contains a module that enables the user to directly download and process some of the most common databases used in deep learning. OpenNIG is freely available via GitHub.","PeriodicalId":405841,"journal":{"name":"2020 13th International Conference on Communications (COMM)","volume":"529 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"OpenNIG - Open Neural Image Generator\",\"authors\":\"Andrei-Marius Avram, Luciana Morogan, Stefan-Adrian Toma\",\"doi\":\"10.1109/COMM48946.2020.9142009\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Generative models are statistical models that learn a true underlying data distribution from samples using unsupervised learning, aiming to generate new data points with some variation. In this paper, we introduce OpenNIG (Open Neural Image Generator), an open-source neural networks toolkit for image generation. It offers the possibility to easily train, validate and test state of the art models. The framework also contains a module that enables the user to directly download and process some of the most common databases used in deep learning. OpenNIG is freely available via GitHub.\",\"PeriodicalId\":405841,\"journal\":{\"name\":\"2020 13th International Conference on Communications (COMM)\",\"volume\":\"529 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 13th International Conference on Communications (COMM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COMM48946.2020.9142009\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 13th International Conference on Communications (COMM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMM48946.2020.9142009","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Generative models are statistical models that learn a true underlying data distribution from samples using unsupervised learning, aiming to generate new data points with some variation. In this paper, we introduce OpenNIG (Open Neural Image Generator), an open-source neural networks toolkit for image generation. It offers the possibility to easily train, validate and test state of the art models. The framework also contains a module that enables the user to directly download and process some of the most common databases used in deep learning. OpenNIG is freely available via GitHub.