{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,15]],"date-time":"2026-08-15T20:28:22Z","timestamp":1786825702800,"version":"build-2736575974"},"reference-count":71,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"4","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"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":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2022,4,1]]},"DOI":"10.1109\/tpami.2020.3029801","type":"journal-article","created":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T19:42:31Z","timestamp":1602272551000},"page":"2045-2057","source":"Crossref","is-referenced-by-count":25,"title":["Learning Generalized Transformation Equivariant Representations Via AutoEncoding Transformations"],"prefix":"10.1109","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3508-1851","authenticated-orcid":false,"given":"Guo-Jun","family":"Qi","sequence":"first","affiliation":[{"name":"Futurewei Seattle Cloud Lab, Seattle, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Central Florida, Orlando, FL, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Electronic Information Engineering, University of Science and Technology of China, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4435-7098","authenticated-orcid":false,"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Purdue University, West Lafayette, IN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-21735-7_6"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"ref3","first-page":"2990","article-title":"Group equivariant convolutional networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Cohen"},{"key":"ref4","article-title":"Steerable CNNs","author":"Cohen","year":"2016"},{"key":"ref5","article-title":"Group equivariant capsule networks","author":"Lenssen","year":"2018"},{"key":"ref6","article-title":"Improved baselines with momentum contrastive learning","author":"Chen","year":"2020"},{"key":"ref7","article-title":"A simple framework for contrastive learning of visual representations","author":"Chen","year":"2020"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8461251"},{"key":"ref9","article-title":"Enaet: Self-trained ensemble autoencoding transformations for semi-supervised learning","author":"Wang","year":"2019"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/1459359.1459446"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/1404880.1404883"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2010.2046270"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.222"},{"key":"ref14","first-page":"3856","article-title":"Dynamic routing between capsules","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Sabour"},{"key":"ref15","first-page":"1","article-title":"Matrix capsules with EM routing","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Hinton"},{"key":"ref16","first-page":"3","article-title":"Autoencoders, minimum description length and helmholtz free energy","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Hinton"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015691"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/1390156.1390294"},{"key":"ref19","article-title":"Auto-encoding variational bayes","author":"Kingma","year":"2013"},{"key":"ref20","article-title":"beta-vae: Learning basic visual concepts with a constrained variational framework","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Higgins"},{"key":"ref21","first-page":"833","article-title":"Contractive auto-encoders: Explicit invariance during feature extraction","volume-title":"Proc. 28th Int. Conf. Int. Conf. Mach. Learn.","author":"Rifai"},{"key":"ref22","first-page":"2672","article-title":"Generative adversarial nets","volume-title":"Proc. Neural Inf. Process. Syst.","author":"Goodfellow"},{"key":"ref23","article-title":"Adversarial feature learning","author":"Donahue","year":"2016"},{"key":"ref24","article-title":"Adversarially learned inference","author":"Dumoulin","year":"2016"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01265-2"},{"key":"ref26","article-title":"Wasserstein gan","author":"Arjovsky","year":"2017"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01228-1_6"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46466-4_5"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.167"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.628"},{"key":"ref31","article-title":"Unsupervised representation learning by predicting image rotations","author":"Gidaris","year":"2018"},{"key":"ref32","first-page":"766","article-title":"Discriminative unsupervised feature learning with convolutional neural networks","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Dosovitskiy"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.13"},{"key":"ref34","article-title":"Temporal ensembling for semi-supervised learning","author":"Laine","year":"2016"},{"key":"ref35","first-page":"1195","article-title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Tarvainen"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01265-2"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/1291233.1291296"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00164"},{"key":"ref40","first-page":"201","article-title":"The IM algorithm: A variational approach to information maximization","volume-title":"Proc. Int. Neural Inf. Process. Syst.","volume":"16","author":"Agakov"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00265"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298904"},{"key":"ref43","article-title":"Unsupervised representation learning with deep convolutional generative adversarial networks","author":"Radford","year":"2015"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.599"},{"key":"ref45","article-title":"Unsupervised learning by predicting noise","author":"Bojanowski","year":"2017"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.320"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_9"},{"key":"ref49","article-title":"Data-dependent initializations of convolutional neural networks","author":"Kr\u00e4henb\u00fchl","year":"2015"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.278"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.76"},{"key":"ref52","first-page":"487","article-title":"Learning deep features for scene recognition using places database","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Zhou"},{"key":"ref53","first-page":"91","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume-title":"Proc. Int. Neural Inf. Process. Syst.","author":"Ren"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00393"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00610"},{"key":"ref57","article-title":"Learning multiple layers of features from tiny images","author":"Krizhevsky","year":"2009"},{"key":"ref58","article-title":"Reading digits in natural images with unsupervised feature learning","author":"Netzer","year":"2011"},{"key":"ref59","first-page":"2234","article-title":"Improved techniques for training gans","volume-title":"Proc. Int. Conf. Neural Inf. Process. Syst.","author":"Salimans"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.249"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-00934-2_84"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2003.1203207"},{"key":"ref63","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00053"},{"key":"ref64","article-title":"Flat: Few-shot learning via autoencoding transformation regularizers","author":"Xu","year":"2019"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2587643"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2016.2607345"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01240-3_31"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2014.115"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1145\/1631272.1631307"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2013.6544875"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1145\/2733373.2806216"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/9729045\/09219238.pdf?arnumber=9219238","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T23:34:17Z","timestamp":1704843257000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9219238\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,1]]},"references-count":71,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.1109\/tpami.2020.3029801","relation":{},"ISSN":["0162-8828","2160-9292","1939-3539"],"issn-type":[{"value":"0162-8828","type":"print"},{"value":"2160-9292","type":"electronic"},{"value":"1939-3539","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,1]]}}}