{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:20:34Z","timestamp":1772554834935,"version":"3.50.1"},"reference-count":58,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Pattern Anal. Mach. Intell."],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/tpami.2021.3062849","type":"journal-article","created":{"date-parts":[[2021,3,2]],"date-time":"2021-03-02T21:17:16Z","timestamp":1614719836000},"page":"1-1","source":"Crossref","is-referenced-by-count":11,"title":["iFlowGAN: An Invertible Flow-based Generative Adversarial Network For Unsupervised Image-to-Image Translation"],"prefix":"10.1109","author":[{"given":"Longquan","family":"Dai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinhui","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00916"},{"key":"ref3","first-page":"3693","article-title":"Unsupervised attention-guided image-to-image translation","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Mejjati"},{"key":"ref4","first-page":"1989","article-title":"CyCADA: Cycle-consistent adversarial domain adaptation","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Hoffman"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.33"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"ref7","article-title":"Invertible conditional GANs for image editing","author":"Perarnau","year":"2016","journal-title":"arXiv: 1611.06355"},{"key":"ref8","first-page":"613","article-title":"Generating videos with scene dynamics","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Vondrick"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.265"},{"key":"ref10","first-page":"469","article-title":"Coupled generative adversarial networks","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46484-8_31"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.168"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"ref14","first-page":"2672","article-title":"Generative adversarial nets","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Goodfellow"},{"key":"ref15","first-page":"214","article-title":"Wasserstein generative adversarial networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Arjovsky"},{"key":"ref16","article-title":"NICE: Non-linear independent components estimation","author":"Dinh","year":"2015","journal-title":"arXiv: 1410.8516"},{"key":"ref17","first-page":"215","article-title":"Glow: Generative flow with invertible 1x1 convolutions","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Kingma"},{"key":"ref18","article-title":"Density estimation using real NVP","author":"Dinh","year":"2017","journal-title":"arXiv: 1605.08803"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11668"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref21","first-page":"700","article-title":"Unsupervised image-to-image translation networks","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Liu"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-020-05929-w"},{"key":"ref23","article-title":"Sylvester normalizing flows for variational inference","author":"van den Berg","year":"2018","journal-title":"arXiv: 1803.05649"},{"key":"ref24","first-page":"824","article-title":"Continuous-time flows for efficient inference and density estimation","volume-title":"Proc. 35th Int. Conf. Mach. Learn.","author":"Chen"},{"key":"ref25","first-page":"247","article-title":"Higher order statistical decorrelation without information loss","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Deco"},{"key":"ref26","first-page":"2214","article-title":"The reversible residual network: Backpropagation without storing activations","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Gomez"},{"key":"ref27","article-title":"i-RevNet: Deep invertible networks","volume-title":"Proc. Int. Conf. Learn. Representations","author":"Jacobsen"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6420\/aa9a90"},{"key":"ref29","first-page":"6572","article-title":"Neural ordinary differential equations","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Chen"},{"key":"ref30","first-page":"573","article-title":"Invertible residual networks","volume":"97","author":"Behrmann"},{"key":"ref31","article-title":"Lipschitz-margin training: Scalable certification of perturbation invariance for deep neural networks","author":"Tsuzuku","year":"2018","journal-title":"arXiv: 1802.04034"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.2019.2961812"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-10925-7_33"},{"key":"ref34","first-page":"7584","article-title":"Lipschitz generative adversarial nets","volume-title":"Proc. 36th Int. Conf. Mach. Learn.","author":"Zhou"},{"key":"ref35","article-title":"Improving the improved training of wasserstein GANs: A consistency term and its dual effect","author":"Wei"},{"key":"ref36","article-title":"Improved robustness to adversarial examples using Lipschitz regularization of the loss","author":"Finlay","year":"2019","journal-title":"arXiv: 1810.00953"},{"key":"ref37","article-title":"Spectral norm regularization for improving the generalizability of deep learning","author":"Yoshida","year":"2017","journal-title":"arXiv: 1705.10941"},{"key":"ref38","article-title":"Spectral normalization for generative adversarial networks","author":"Miyato"},{"key":"ref39","article-title":"Generalizable adversarial training via spectral normalization","author":"Farnia"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.310"},{"key":"ref41","first-page":"1857","article-title":"Learning to discover cross-domain relations with generative adversarial networks","volume-title":"Proc. 34th Int. Conf. Mach. Learn.","author":"Kim"},{"key":"ref42","first-page":"752","article-title":"One-sided unsupervised domain mapping","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Benaim"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00485"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00473"},{"key":"ref45","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-62010-2"},{"key":"ref46","article-title":"Lipschitz regularized deep neural networks converge and generalize","author":"Oberman","year":"2018","journal-title":"arXiv: 1808.09540"},{"key":"ref47","first-page":"1530","article-title":"Variational inference with normalizing flows","volume-title":"Proc. 32nd Int. Conf. Mach. Learn.","author":"Rezende"},{"key":"ref48","first-page":"5767","article-title":"Improved training of wasserstein GANs","volume-title":"Proc. Advances Neural Inf. Process. Syst.","author":"Gulrajani"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01246-5_4"},{"key":"ref50","article-title":"Large scale GAN training for high fidelity natural image synthesis","author":"Brock","year":"2018","journal-title":"arXiv: 1809.11096"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1016\/b978-1-4832-1446-7.50035-2"},{"key":"ref52","first-page":"6629","article-title":"GANs trained by a two time-scale update rule converge to a local nash equilibrium","volume-title":"Proc. 31st Int. Conf. Neural Inf. Process. Syst.","author":"Heusel"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11829"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40602-7_39"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"ref57","article-title":"Reading digits in natural images with unsupervised feature learning","author":"Netzer","year":"2011"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2572683"}],"container-title":["IEEE Transactions on Pattern Analysis and Machine Intelligence"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/34\/4359286\/09367012.pdf?arnumber=9367012","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T23:55:27Z","timestamp":1704844527000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9367012\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":58,"URL":"https:\/\/doi.org\/10.1109\/tpami.2021.3062849","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":[[2021]]}}}