{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T01:33:17Z","timestamp":1725759197562},"reference-count":40,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,8]]},"DOI":"10.1109\/icpr.2018.8546037","type":"proceedings-article","created":{"date-parts":[[2018,11,30]],"date-time":"2018-11-30T00:17:38Z","timestamp":1543537058000},"page":"2444-2449","source":"Crossref","is-referenced-by-count":2,"title":["Teaching Squeeze-and-Excitation PyramidNet for Imbalanced Image Classification with GAN-based Curriculum Learning"],"prefix":"10.1109","author":[{"given":"Jing","family":"Liu","sequence":"first","affiliation":[]},{"given":"Angang","family":"Du","sequence":"additional","affiliation":[]},{"given":"Chao","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Haiyong","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Nan","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Bing","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"journal-title":"Conditional image synthesis with auxiliary classifier gans","year":"2016","author":"odena","key":"ref39"},{"key":"ref38","article-title":"Visualizing and understanding convolutional networks","author":"zeiler","year":"2014","journal-title":"ECCV"},{"key":"ref33","doi-asserted-by":"crossref","DOI":"10.1609\/aaai.v29i1.9608","article-title":"Self-paced curriculum learning","author":"jiang","year":"2015","journal-title":"AAAI"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"ref31","article-title":"Cost-sensitive learning of deep feature representations from imbalanced data","author":"khan","year":"2017","journal-title":"IEEE TNNLS"},{"key":"ref30","article-title":"Training cost-sensitive neural networks with methods addressing the class imbalance problem","author":"zhou","year":"2006","journal-title":"IEEE TKDE"},{"journal-title":"Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift","year":"2015","author":"ioffe","key":"ref37"},{"key":"ref36","article-title":"Rectified linear units improve restricted boltzmann machines","author":"nair","year":"2010","journal-title":"ICML"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.223"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7299188"},{"journal-title":"Deep pyramidal residual networks","year":"2016","author":"han","key":"ref10"},{"key":"ref40","article-title":"Identity mappings in deep residual networks","author":"he","year":"2016","journal-title":"ECCV"},{"journal-title":"Squeeze-and-Excitation Networks","year":"2017","author":"hu","key":"ref11"},{"key":"ref12","article-title":"Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation","author":"powers","year":"2011","journal-title":"Journal of Machine Learning Technologies"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP.2017.8296402"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCC.2011.2161285"},{"journal-title":"Data augmentation in emotion classification using generative adversarial networks","year":"2017","author":"zhu","key":"ref15"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2013.06.002"},{"key":"ref17","article-title":"Generative adversarial nets","author":"goodfellow","year":"2014","journal-title":"NIPS"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-015-0816-y"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2646371"},{"key":"ref28","article-title":"Relay backpropagation for effective learning of deep convolutional neural networks","author":"shen","year":"2016","journal-title":"ECCV"},{"journal-title":"WHOI-Plankton-a large scale fine grained visual recognition benchmark dataset for plankton classification","year":"2015","author":"orenstein","key":"ref4"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007736"},{"key":"ref3","article-title":"Learning from imbalanced data","author":"he","year":"2009","journal-title":"IEEE TKDE"},{"key":"ref6","article-title":"ImageNet classification with deep convolutional neural networks","author":"krizhevsky","year":"2012","journal-title":"NIPS"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2008.06.108"},{"key":"ref5","article-title":"Caltech-256 object category dataset","author":"griffin","year":"2007","journal-title":"Technical Report 7694 California Institute of Technology"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref7","article-title":"Very deep convolutional networks for large-scale image recognition","author":"simonyan","year":"2015","journal-title":"ICLRE"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/1102256.1102271"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"journal-title":"A systematic study of the class imbalance problem in convolutional neural networks","year":"2017","author":"buda","key":"ref1"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref22","article-title":"Exploratory undersampling for class-imbalance learning","author":"liu","year":"2009","journal-title":"IEEE TSMC"},{"key":"ref21","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-540-27868-9_88","article-title":"The imbalanced training sample problem: under or over sampling?","author":"barandela","year":"2004","journal-title":"Structural Syntactic and Statistical Pattern Recognition"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW.2015.7301352"},{"key":"ref23","article-title":"Learning from class-imbalanced data: review of methods and applications","author":"haixiang","year":"2016","journal-title":"Expert Systems with Applications"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2012.232"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1613\/jair.953"}],"event":{"name":"2018 24th International Conference on Pattern Recognition (ICPR)","start":{"date-parts":[[2018,8,20]]},"location":"Beijing","end":{"date-parts":[[2018,8,24]]}},"container-title":["2018 24th International Conference on Pattern Recognition (ICPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/8527858\/8545020\/08546037.pdf?arnumber=8546037","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T19:03:19Z","timestamp":1694113399000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8546037\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,8]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/icpr.2018.8546037","relation":{},"subject":[],"published":{"date-parts":[[2018,8]]}}}