Pub Date : 2024-09-16DOI: 10.1109/mmul.2024.3448100
Sristi Dakshit, Balakrishnan Prabhakaran
GANs are a class of machine learning framework that are used to generate new data instances that resemble the training data. First proposed by Goodfellow et al.,1 the GAN architecture (Figure 1) typically consists of two separate competing adversarial neural networks that learn from each other. The two neural networks in a GAN model are a generator model, which creates new synthetic data samples, and a discriminator model, which evaluates the synthetic samples generated by the generator model against real data samples. The evaluation of synthetic data by the discriminator helps the generator create better and more realistic, accurate samples. As the generator improves, so does the discriminator, enabling effective learning with the aim of producing synthetic data that are so realistic that the discriminator cannot tell if they are real or fake.
GAN 是一类机器学习框架，用于生成与训练数据相似的新数据实例。GAN 架构（图 1）由 Goodfellow 等人1 首次提出，通常由两个相互竞争、相互学习的对抗神经网络组成。GAN 模型中的两个神经网络分别是生成器模型和判别器模型，前者负责创建新的合成数据样本，后者负责将生成器模型生成的合成样本与真实数据样本进行对比评估。鉴别器对合成数据的评估有助于生成器创建更好、更真实、更准确的样本。随着生成器的改进，鉴别器也会随之改进，从而实现有效的学习，目的是生成逼真到鉴别器无法辨别真假的合成数据。
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Pub Date : 2024-09-13DOI: 10.1109/mmul.2024.3459003
Wencheng Gu, Li Sun, Zhipeng Jiang, Kexue Sun
{"title":"High-performance Embedded System Design for QR Code Recognition with Deep Learning","authors":"Wencheng Gu, Li Sun, Zhipeng Jiang, Kexue Sun","doi":"10.1109/mmul.2024.3459003","DOIUrl":"https://doi.org/10.1109/mmul.2024.3459003","url":null,"abstract":"","PeriodicalId":13240,"journal":{"name":"IEEE MultiMedia","volume":"4 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Development of an Image Encryption Algorithm Based on Compressed Sensing and Chaotic Mapping","authors":"Shaohui Yan, Yu Cui, Lin Li, Yuyan Zhang, Defeng Jiang, Hanbing Zhang","doi":"10.1109/mmul.2024.3428484","DOIUrl":"https://doi.org/10.1109/mmul.2024.3428484","url":null,"abstract":"","PeriodicalId":13240,"journal":{"name":"IEEE MultiMedia","volume":"136 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141739960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-11DOI: 10.1109/mmul.2024.3414101
{"title":"Get Published in the New IEEE Transactions on Privacy","authors":"","doi":"10.1109/mmul.2024.3414101","DOIUrl":"https://doi.org/10.1109/mmul.2024.3414101","url":null,"abstract":"","PeriodicalId":13240,"journal":{"name":"IEEE MultiMedia","volume":"48 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141610235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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