{"id":72872,"date":"2022-04-01T10:51:59","date_gmt":"2022-04-01T01:51:59","guid":{"rendered":"https:\/\/www.waseda.jp\/inst\/research\/?p=72872"},"modified":"2022-07-11T16:11:15","modified_gmt":"2022-07-11T07:11:15","slug":"distribution-bias-aware-collaborative-generative-adversarial-network-for-imbalanced-deep-learning-in-industrial-iot%ef%bc%882022%e5%b9%b44%e6%9c%88%ef%bc%9aieee-transactions-on-industrial-informatics","status":"publish","type":"post","link":"https:\/\/www.waseda.jp\/inst\/research\/news\/72872","title":{"rendered":"Distribution Bias Aware Collaborative Generative Adversarial Network for Imbalanced Deep Learning in Industrial IoT\uff08Published in IEEE Transactions on Industrial Informatics, April 2022\uff09"},"content":{"rendered":"<table class=\"table table-bordered table-colored-tbhd\" style=\"height: 550px; width: 100%; border-collapse: collapse; border-style: solid;\" border=\"1\">\n<tbody>\n<tr style=\"height: 78px;\">\n<td style=\"width: 19.5804%; height: 78px;\">Journal Title<br \/>\n\/\u63b2\u8f09\u30b8\u30e3\u30fc\u30ca\u30eb\u540d<\/td>\n<td style=\"width: 68.3876%; height: 78px;\">IEEE Transactions on Industrial Informatics<\/td>\n<\/tr>\n<tr style=\"height: 65px;\">\n<td style=\"width: 19.5804%; height: 80px;\">Publication Year and Month<br \/>\n\/\u63b2\u8f09\u5e74\u6708<\/td>\n<td style=\"width: 68.3876%; height: 80px;\">April, 2022<\/td>\n<\/tr>\n<tr style=\"height: 55px;\">\n<td style=\"width: 19.5804%; height: 79px;\">Paper Title<br \/>\n\/\u8ad6\u6587\u30bf\u30a4\u30c8\u30eb<\/td>\n<td style=\"width: 68.3876%; height: 79px;\">Distribution Bias Aware Collaborative Generative Adversarial Network for Imbalanced Deep Learning in Industrial IoT<\/td>\n<\/tr>\n<tr style=\"height: 85px;\">\n<td style=\"width: 19.5804%; height: 85px;\">DOI<br \/>\n\/\u8ad6\u6587DOI<\/td>\n<td style=\"width: 68.3876%; height: 85px;\"><a href=\"https:\/\/doi.org\/10.1109\/TII.2022.3170149\">10.1109\/TII.2022.3170149<\/a><\/td>\n<\/tr>\n<tr style=\"height: 59px;\">\n<td style=\"width: 19.5804%; height: 80px;\">Author of Waseda University<br \/>\n\/\u672c\u5b66\u306e\u8457\u8005<\/td>\n<td style=\"width: 68.3876%; height: 80px;\">JIN, Qun (Professor, Faculty of Human Sciences, School of Human Sciences): Last Author<\/p>\n<p><a href=\"https:\/\/w-rdb.waseda.jp\/html\/100000696_ja.html\">\u65e9\u7a32\u7530\u5927\u5b66\u7814\u7a76\u8005\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\uff08\u65e5\u672c\u8a9e\uff09<\/a><\/p>\n<p><a href=\"https:\/\/w-rdb.waseda.jp\/html\/100000696_en.html\">Waseda University Researchers Database(English)<\/a><\/td>\n<\/tr>\n<tr style=\"height: 68px;\">\n<td style=\"width: 19.5804%; height: 86px;\">Related Websites<br \/>\n\/\u95a2\u9023Web<\/td>\n<td style=\"width: 68.3876%; height: 86px;\">&#8211;<\/td>\n<\/tr>\n<tr style=\"height: 138px;\">\n<td style=\"width: 19.5804%; height: 148px;\">Abstract<br \/>\n\/\u6284\u9332<\/td>\n<td style=\"width: 68.3876%; height: 148px;\">In this study, we propose a DB-CGAN model to deal with distribution bias issues for imbalanced deep learning in industrial IoT. An integrated data augmentation framework is constructed based on introduction of a complementary classifier into the basic GAN model. Specifically, a conditional generator with random labels is designed and trained adversarially with the classifier to effectively enhance the augmentation of number of data samples in minority classes, while a weight sharing scheme is newly devised between two separated feature extractors, enabling the collaborative adversarial training among generator, discriminator, and classifier. An augmentation algorithm is then developed to facilitate intelligent anomaly detection, which can efficiently improve the classification accuracy based on the correction of distribution bias. Experiment evaluations based on two real-world imbalanced datasets demonstrate the outstanding performance of our proposed model in tackling distribution bias issues for imbalanced learning in industrial IoT applications, compared with five baseline methods.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n","protected":false},"excerpt":{"rendered":"<p>Journal Title \/\u63b2\u8f09\u30b8\u30e3\u30fc\u30ca\u30eb\u540d IEEE Transactions on Industrial Informatics Publication Year and Month \/\u63b2\u8f09\u5e74\u6708 April, 20 [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[],"tags":[218,217],"class_list":["post-72872","post","type-post","status-publish","format-standard","hentry","tag-impact-en","tag-impact"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.waseda.jp\/inst\/research\/wp-json\/wp\/v2\/posts\/72872","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.waseda.jp\/inst\/research\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.waseda.jp\/inst\/research\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.waseda.jp\/inst\/research\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.waseda.jp\/inst\/research\/wp-json\/wp\/v2\/comments?post=72872"}],"version-history":[{"count":2,"href":"https:\/\/www.waseda.jp\/inst\/research\/wp-json\/wp\/v2\/posts\/72872\/revisions"}],"predecessor-version":[{"id":73016,"href":"https:\/\/www.waseda.jp\/inst\/research\/wp-json\/wp\/v2\/posts\/72872\/revisions\/73016"}],"wp:attachment":[{"href":"https:\/\/www.waseda.jp\/inst\/research\/wp-json\/wp\/v2\/media?parent=72872"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.waseda.jp\/inst\/research\/wp-json\/wp\/v2\/categories?post=72872"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.waseda.jp\/inst\/research\/wp-json\/wp\/v2\/tags?post=72872"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}