{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T01:59:40Z","timestamp":1729648780100,"version":"3.28.0"},"reference-count":43,"publisher":"IEEE","license":[{"start":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T00:00:00Z","timestamp":1559347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T00:00:00Z","timestamp":1559347200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2019,6,1]],"date-time":"2019-06-01T00:00:00Z","timestamp":1559347200000},"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":[[2019,6]]},"DOI":"10.1109\/cvpr.2019.01033","type":"proceedings-article","created":{"date-parts":[[2020,1,10]],"date-time":"2020-01-10T02:06:13Z","timestamp":1578621973000},"page":"10083-10092","source":"Crossref","is-referenced-by-count":23,"title":["Enhancing TripleGAN for Semi-Supervised Conditional Instance Synthesis and Classification"],"prefix":"10.1109","author":[{"given":"Si","family":"Wu","sequence":"first","affiliation":[]},{"given":"Guangchang","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Jichang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Zhiwen","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Hau-San","family":"Wong","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2017.2772836"},{"journal-title":"Proc International Conference on Learning Representations","article-title":"Improving the improved training of Wasserstein GANs: a consistency term and its dual effect","year":"2018","author":"wei","key":"ref38"},{"journal-title":"Fundations of Statistical Natural Language Processing","year":"1999","author":"schutze","key":"ref33"},{"key":"ref32","first-page":"2234","article-title":"Improved techniques for training GANs","author":"salimans","year":"2016","journal-title":"Proc Neural Info Process Syst"},{"journal-title":"Proc Advances in Neural Information Processing Systems","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","year":"2015","author":"ren","key":"ref31"},{"key":"ref30","first-page":"3546","article-title":"Semi-supervised learning with ladder networks","author":"rasmus","year":"2015","journal-title":"Proc Neural Info Process Syst"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-12-802806-3.00008-7"},{"journal-title":"Proc Advances in Neural Information Processing Systems","article-title":"Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results","year":"2017","author":"tarvainen","key":"ref36"},{"journal-title":"Proc International Conference on Learning Representations","article-title":"Unsupervised and semi-supervised learning with categorical generative adversarial networks","year":"2016","author":"springenberg","key":"ref35"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2572683"},{"key":"ref10","first-page":"3581","article-title":"Semi-supervised learning with deep generative models","author":"kingma","year":"2017","journal-title":"Proc Neural Info Process Syst"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2017.2758522"},{"journal-title":"International Conference on Learning Representations","article-title":"Auto-encoding variational Bayes","year":"2014","author":"kingma","key":"ref11"},{"journal-title":"International Conference on Learning Representations","article-title":"Semi-supervised classification with graph convolutional networks","year":"2017","author":"kipf","key":"ref12"},{"article-title":"Learning multiple layers of features from tiny images","year":"2009","author":"krizhevsky","key":"ref13"},{"key":"ref14","first-page":"1106","article-title":"Imagenet classification with deep convolutional neural networks","author":"krizhevsky","year":"2014","journal-title":"Proc Neural Info Process Syst"},{"journal-title":"Proc International Conference on Learning Representations","article-title":"Temporal ensembling for semisupervised learning","year":"2017","author":"laine","key":"ref15"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref17","first-page":"1195","article-title":"Triple generative adversarial nets","author":"li","year":"2017","journal-title":"Proc Advances in Neural Information Processing Systems"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2017.2766142"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00927"},{"key":"ref28","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v32i1.11634","article-title":"Adversarial dropout for supervised and semi-supervised learning","author":"park","year":"2018","journal-title":"Proc AAAI Conference on Artificial Intelligence"},{"key":"ref4","first-page":"6513","article-title":"Good semi-supervised learning that requires a bad GAN","author":"dai","year":"2017","journal-title":"Proc Advances in Neural Information Processing Systems"},{"journal-title":"International Conference on Learning Representations","article-title":"Semi-supervised learning with generative adversarial networks","year":"2016","author":"odena","key":"ref27"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.299"},{"key":"ref6","first-page":"258","article-title":"Training generative neural networks via maximum mean discrepancy optimization","author":"dziugaite","year":"2015","journal-title":"Proc Conference on Uncertainty in Artificial Intelligence"},{"journal-title":"International Conference on Learning Representations","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","year":"2016","author":"radford","key":"ref29"},{"journal-title":"International Conference on Learning Representations","article-title":"Adversarially learned inference","year":"2017","author":"dumoulin","key":"ref5"},{"key":"ref8","first-page":"2672","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"Proc Advances in Neural Information Processing Systems"},{"journal-title":"Proc Advances in Neural Information Processing Systems","article-title":"Triangle generative adversarial networks","year":"2017","author":"gan","key":"ref7"},{"key":"ref2","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"arjovsky","year":"2017","journal-title":"Proc International Conference on Machine Learning"},{"journal-title":"Proc International Conference on Learning Representations","article-title":"Adam: a method for stochastic optimization","year":"2015","author":"kingma","key":"ref9"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.90"},{"key":"ref20","first-page":"2579","article-title":"Visualizing data using t-sne","volume":"9","author":"maaten","year":"2008","journal-title":"Journal of Machine Learning Research"},{"journal-title":"International Conference on Learning Representations","article-title":"cGANs with projection discriminator","year":"2018","author":"miyato","key":"ref22"},{"journal-title":"International Conference on Learning Representations","article-title":"Spectral normalization for generative adversarial networks","year":"2018","author":"miyato","key":"ref21"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2018.2840738"},{"journal-title":"Proc International Conference on Learning Representations","article-title":"Distributional smoothing with virtual adversarial training","year":"2016","author":"miyato","key":"ref24"},{"journal-title":"Proc International Conference on Machine Learning","article-title":"Revisiting semi-supervised learning with graph embeddings","year":"2016","author":"yang","key":"ref41"},{"journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence (Early Access)","article-title":"Virtual adversarial training: a regularization method for supervised and semi-supervised learning","year":"2018","author":"miyato","key":"ref23"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2014.7025068"},{"article-title":"Selfattention generative adversarial networks","year":"2018","author":"zhang","key":"ref43"},{"journal-title":"Proc NIPS Workshop on Deep Learning and Unsupervised Feature Learning","article-title":"Reading digits in natural images with unsupervised feature learning","year":"2011","author":"netzer","key":"ref25"}],"event":{"name":"2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","start":{"date-parts":[[2019,6,15]]},"location":"Long Beach, CA, USA","end":{"date-parts":[[2019,6,20]]}},"container-title":["2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8938205\/8953184\/08953685.pdf?arnumber=8953685","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,24]],"date-time":"2023-09-24T22:57:10Z","timestamp":1695596230000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8953685\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6]]},"references-count":43,"URL":"https:\/\/doi.org\/10.1109\/cvpr.2019.01033","relation":{},"subject":[],"published":{"date-parts":[[2019,6]]}}}