{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:07:58Z","timestamp":1760238478306,"version":"build-2065373602"},"reference-count":41,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,8,17]],"date-time":"2020-08-17T00:00:00Z","timestamp":1597622400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61672150, 61702092, 61907007"],"award-info":[{"award-number":["61672150, 61702092, 61907007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jilin Provincial Science and Technology Department Project","award":["20180201089GX, 20200401081GX, 20200401086GX, 20190201305JC, 20190303129SF, 20200201199JC"],"award-info":[{"award-number":["20180201089GX, 20200401081GX, 20200401086GX, 20190201305JC, 20190303129SF, 20200201199JC"]}]},{"name":"Provincial Department of Education Project","award":["JJKH20190291KJ, JJKH20190294KJ, JJKH20190355KJ"],"award-info":[{"award-number":["JJKH20190291KJ, JJKH20190294KJ, JJKH20190355KJ"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["No. 2412019FZ049"],"award-info":[{"award-number":["No. 2412019FZ049"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Artificial intelligence is one of the most popular topics in computer science. Convolutional neural network (CNN), which is an important artificial intelligence deep learning model, has been widely used in many fields. However, training a CNN requires a large amount of labeled data to achieve a good performance but labeling data is a time-consuming and laborious work. Since active learning can effectively reduce the labeling effort, we propose a new intelligent active learning method for deep learning, which is called multi-view active learning based on double-branch network (MALDB). Different from most existing active learning methods, our proposed MALDB first integrates two Bayesian convolutional neural networks (BCNNs) with different structures as two branches of a classifier to learn the effective features for each sample. Then, MALDB performs data analysis on unlabeled dataset and queries the useful unlabeled samples based on different characteristics of two branches to iteratively expand the training dataset and improve the performance of classifier. Finally, MALDB combines multiple level information from multiple hidden layers of BCNNs to further improve the stability of sample selection. The experiments are conducted on five extensively used datasets, Fashion-MNIST, Cifar-10, SVHN, Scene-15 and UIUC-Sports, the experimental results demonstrate the validity of our proposed MALDB.<\/jats:p>","DOI":"10.3390\/e22080901","type":"journal-article","created":{"date-parts":[[2020,8,17]],"date-time":"2020-08-17T21:58:53Z","timestamp":1597701533000},"page":"901","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Intelligent Multi-View Active Learning Method Based on a Double-Branch Network"],"prefix":"10.3390","volume":"22","author":[{"given":"Fucong","family":"Liu","sequence":"first","affiliation":[{"name":"College of Information Sciences and Technology, Northeast Normal University, Changchun 130117, China"}]},{"given":"Tongzhou","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Sciences and Technology, Northeast Normal University, Changchun 130117, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4028-6149","authenticated-orcid":false,"given":"Caixia","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Information Sciences and Technology, Northeast Normal University, Changchun 130117, China"},{"name":"Institute for Intelligent Elderlycare, College of Humanities and Sciences, Northeast Normal University, Changchun 130117, China"}]},{"given":"Yuanyuan","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Information Sciences and Technology, Northeast Normal University, Changchun 130117, China"}]},{"given":"Xiaoli","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Chemical &amp; Biomolecular Engineering, National University of Singapore, Singapore 117585, Singapore"}]},{"given":"Miao","family":"Qi","sequence":"additional","affiliation":[{"name":"College of Information Sciences and Technology, Northeast Normal University, Changchun 130117, China"}]},{"given":"Jun","family":"Kong","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Elderlycare, College of Humanities and Sciences, Northeast Normal University, Changchun 130117, China"}]},{"given":"Jianzhong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Sciences and Technology, Northeast Normal University, Changchun 130117, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,17]]},"reference":[{"key":"ref_1","first-page":"679","article-title":"A dual-chaining watermark scheme for data integrity protection in Internet of Things","volume":"58","author":"Wang","year":"2019","journal-title":"CMC Comput. 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