{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,5]],"date-time":"2026-11-05T13:09:43Z","timestamp":1793884183943,"version":"4.7.0"},"reference-count":55,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T00:00:00Z","timestamp":1610236800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100004520","name":"Tokyo Institute of Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004520","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,1,10]]},"DOI":"10.1109\/icpr48806.2021.9412082","type":"proceedings-article","created":{"date-parts":[[2021,5,6]],"date-time":"2021-05-06T02:15:54Z","timestamp":1620267354000},"page":"362-369","source":"Crossref","is-referenced-by-count":4,"title":["Augmented Cyclic Consistency Regularization for Unpaired Image-to-Image Translation"],"prefix":"10.1109","author":[{"given":"Takehiko","family":"Ohkawa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naoto","family":"Inoue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hirokatsu","family":"Kataoka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nakamasa","family":"Inoue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Stabilizing Training of Generative Adversarial Networks through Regularization","author":"roth","year":"0","journal-title":"Proc NeurIPS"},{"key":"ref38","author":"kodali","year":"2017","journal-title":"On convergence and stability of gans"},{"key":"ref33","author":"ba","year":"2016","journal-title":"Layer normalization"},{"key":"ref32","author":"ioffe","year":"2015","journal-title":"Batch Normalization Accelerating Deep Network Training by Reducing Internal Covariate Shift"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00820"},{"key":"ref30","article-title":"U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation","author":"kim","year":"0","journal-title":"Proc ICLR"},{"key":"ref37","article-title":"Improved Training of Wasserstein GANs","author":"gulrajani","year":"0","journal-title":"Proc NeurIPS"},{"key":"ref36","article-title":"A Large-Scale Study on Regularization and Normalization in GANs","author":"kurach","year":"0","journal-title":"Proc ICML"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.304"},{"key":"ref34","article-title":"Spectral Normalization for Generative Adversarial Networks","author":"miyato","year":"0","journal-title":"Proc ICLR"},{"key":"ref28","article-title":"Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks","author":"nam","year":"0","journal-title":"Proc NeurIPS"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.167"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00244"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2439281"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.278"},{"key":"ref20","article-title":"Unsupervised Image-to-Image Translation Networks","author":"liu","year":"0","journal-title":"Proc NeurIPS"},{"key":"ref22","article-title":"Multimodal Unsupervised Image-to-Image Translation","author":"huang","year":"0","journal-title":"Proc ECCV"},{"key":"ref21","article-title":"Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data","author":"almahairi","year":"0","journal-title":"Proc ICML"},{"key":"ref24","article-title":"Diverse Image-to- Image Translation via Disentangled Representations","author":"lee","year":"0","journal-title":"Proc ECCV"},{"key":"ref23","article-title":"Exemplar Guided Unsupervised Image-to-Image Translation with Semantic Consistency","author":"ma","year":"0","journal-title":"Proc ICLR"},{"key":"ref26","article-title":"A Learned Representation for Artistic Style","author":"dumoulin","year":"0","journal-title":"Proc ICLR"},{"key":"ref25","author":"ulyanov","year":"2016","journal-title":"Instance normalization The missing ingredient for fast stylization"},{"key":"ref50","article-title":"Adam: A Method for Stochastic Optimization","author":"kingma","year":"0","journal-title":"Proc ICLR"},{"key":"ref51","article-title":"Dropout: a simple way to prevent neural networks from overfitting","author":"srivastava","year":"2014","journal-title":"The Journal of Machine Learning Research"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"ref54","article-title":"Learning Discrete Representations via Information Maximizing Self-Augmented Training","author":"hu","year":"0","journal-title":"Proc ICML"},{"key":"ref53","article-title":"Temporal Ensembling for Semi-Supervised Learning","author":"laine","year":"0","journal-title":"Proc ICLR"},{"key":"ref52","article-title":"Regularization with stochastic transformations and perturbations for deep semi-supervised learning","author":"sajjadi","year":"0","journal-title":"Proc NeurIPS"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00541"},{"key":"ref11","article-title":"Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person reidentification","author":"deng","year":"0","journal-title":"Proc CVPR"},{"key":"ref40","article-title":"Consistency Regularization for Generative Adversarial Networks","author":"zhang","year":"0","journal-title":"Proc ICLR"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00016"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00069"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00224"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref17","article-title":"Toward Multimodal Image-to-Image Translation","author":"zhu","year":"0","journal-title":"Proc NeurIPS"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref19","article-title":"Learning to Discover Cross-Domain Relations with Generative Adversarial Networks","author":"kim","year":"0","journal-title":"Proc ICML"},{"key":"ref4","article-title":"Colorful Image Colorization","author":"zhang","year":"0","journal-title":"Proc ECCV"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref6","article-title":"CyCADA: Cycle-Consistent Adversarial Domain Adaptation","author":"hoffman","year":"0","journal-title":"Proc ICML"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.265"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00145"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00473"},{"key":"ref49","author":"zhong","year":"2017","journal-title":"Random Erasing Data Augmentation"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00845"},{"key":"ref46","article-title":"Reading Digits in Natural Images with Unsupervised Feature Learning","author":"netzer","year":"0","journal-title":"Proc NeurIPSW"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2010.161"},{"key":"ref48","author":"devries","year":"2017","journal-title":"Improved regularization of convolutional neural networks with cutout"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref42","article-title":"Augmented Cyclic Adversarial Learning for Low Resource Domain. Adaptation","author":"asl","year":"0","journal-title":"Proc ICLR"},{"key":"ref41","article-title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","author":"radford","year":"0","journal-title":"Proc ICLR"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-58347-1_10"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"}],"event":{"name":"2020 25th International Conference on Pattern Recognition (ICPR)","location":"Milan, Italy","start":{"date-parts":[[2021,1,10]]},"end":{"date-parts":[[2021,1,15]]}},"container-title":["2020 25th International Conference on Pattern Recognition (ICPR)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9411940\/9411911\/09412082.pdf?arnumber=9412082","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:40:38Z","timestamp":1652197238000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9412082\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,10]]},"references-count":55,"URL":"https:\/\/doi.org\/10.1109\/icpr48806.2021.9412082","relation":{},"subject":[],"published":{"date-parts":[[2021,1,10]]}}}