{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T15:01:13Z","timestamp":1725807673811},"reference-count":30,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,5,1]],"date-time":"2021-05-01T00:00:00Z","timestamp":1619827200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5]]},"DOI":"10.1109\/iscas51556.2021.9401169","type":"proceedings-article","created":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T17:33:36Z","timestamp":1619544816000},"page":"1-5","source":"Crossref","is-referenced-by-count":3,"title":["Improving FM-GAN through Mixup Manifold Regularization"],"prefix":"10.1109","author":[{"given":"Farzin","family":"Ghorban","sequence":"first","affiliation":[]},{"given":"Nesreen","family":"Hasan","sequence":"additional","affiliation":[]},{"given":"Jorg","family":"Velten","sequence":"additional","affiliation":[]},{"given":"Anton","family":"Kummert","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref30","article-title":"mixup: Beyond empirical risk minimization","author":"zhang","year":"2017","journal-title":"CoRR"},{"doi-asserted-by":"publisher","key":"ref10","DOI":"10.1109\/ICCV.2019.00683"},{"year":"2014","author":"kingma","article-title":"Adam: A method for stochastic optimization","key":"ref11"},{"year":"2009","author":"krizhevsky","journal-title":"Learning multiple layers of features from tiny images","key":"ref12"},{"year":"2016","author":"laine","article-title":"Temporal ensembling for semi-supervised learning","key":"ref13"},{"year":"2018","author":"lecouat","article-title":"Semi-supervised learning with gans: Revisiting manifold regularization","key":"ref14"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.1109\/5.726791"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.1109\/CVPR.2018.00927"},{"key":"ref17","article-title":"Visualizing data using t-sne","author":"maaten","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"ref18","article-title":"Reading digits in natural images with unsupervised feature learning","author":"netzer","year":"2011","journal-title":"Advances in neural information processing systems"},{"year":"2016","author":"odena","article-title":"Semi-supervised learning with generative adversarial networks","key":"ref19"},{"year":"2018","author":"wei","article-title":"Improving the improved training of wasserstein gans: A consistency term and its dual effect","key":"ref28"},{"key":"ref4","article-title":"Triple generative adversarial nets","author":"chongxuan","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref27","article-title":"Weight-averaged consistency targets improve semi-supervised deep learning results","author":"tarvainen","year":"2017","journal-title":"CoRR"},{"year":"2020","author":"chen","article-title":"Consistency regularization with generative adversarial networks for semi-supervised image classification","key":"ref3"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1007\/s13748-018-0149-5"},{"doi-asserted-by":"publisher","key":"ref29","DOI":"10.1109\/IJCNN.2019.8851712"},{"key":"ref5","article-title":"Good semi-supervised learning that requires a bad gan","author":"dai","year":"2017","journal-title":"Advances in neural information processing systems"},{"doi-asserted-by":"publisher","key":"ref8","DOI":"10.1109\/CVPR.2019.00521"},{"key":"ref7","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Advances in neural information processing systems"},{"key":"ref2","article-title":"There are many consistent explanations of unlabeled data: Why you should average","author":"athiwaratkun","year":"2019","journal-title":"7th International Conference on Learning Representations ICLR 2019"},{"key":"ref9","article-title":"Semi-supervised learning via compact latent space clustering","author":"kamnitsas","year":"2018","journal-title":"ICML"},{"key":"ref1","article-title":"Tensorflow: A system for large-scale machine learning","author":"abadi","year":"2016","journal-title":"Symposium on Operating System Design and Implementation"},{"doi-asserted-by":"publisher","key":"ref20","DOI":"10.1007\/s11263-019-01265-2"},{"key":"ref22","article-title":"Semi-supervised learning with ladder networks","author":"rasmus","year":"2015","journal-title":"Advances in neural information processing systems"},{"year":"2015","author":"radford","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","key":"ref21"},{"key":"ref24","article-title":"Regularization with stochastic transformations and perturbations for deep semi-supervised learning","author":"sajjadi","year":"2016","journal-title":"Advances in neural information processing systems"},{"key":"ref23","article-title":"The manifold tangent classifier","author":"rifai","year":"2011","journal-title":"Advances in neural information processing systems"},{"key":"ref26","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","author":"tarvainen","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref25","article-title":"Improved techniques for training gans","author":"salimans","year":"2016","journal-title":"Advances in neural information processing systems"}],"event":{"name":"2021 IEEE International Symposium on Circuits and Systems (ISCAS)","start":{"date-parts":[[2021,5,22]]},"location":"Daegu, Korea","end":{"date-parts":[[2021,5,28]]}},"container-title":["2021 IEEE International Symposium on Circuits and Systems (ISCAS)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9401028\/9401051\/09401169.pdf?arnumber=9401169","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T11:44:12Z","timestamp":1652183052000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9401169\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/iscas51556.2021.9401169","relation":{},"subject":[],"published":{"date-parts":[[2021,5]]}}}