{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T16:35:55Z","timestamp":1764174955868,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030584511"},{"type":"electronic","value":"9783030584528"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58452-8_33","type":"book-chapter","created":{"date-parts":[[2020,11,3]],"date-time":"2020-11-03T00:34:03Z","timestamp":1604363643000},"page":"565-581","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Regularization with Latent Space Virtual Adversarial Training"],"prefix":"10.1007","author":[{"given":"Genki","family":"Osada","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Budrul","family":"Ahsan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Revoti Prasad","family":"Bora","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takashi","family":"Nishide","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,11,3]]},"reference":[{"key":"33_CR1","unstructured":"Athiwaratkun, B., Finzi, M., Izmailov, P., Wilson, A.G.: There are many consistent explanations of unlabeled data: Why you should average. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=rkgKBhA5Y7"},{"key":"33_CR2","unstructured":"Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.: MixMatch: a holistic approach to semi-supervised learning. In: NeurIPS (2019)"},{"key":"33_CR3","doi-asserted-by":"crossref","unstructured":"Cao, X., Gong, N.Z.: Mitigating evasion attacks to deep neural networks via region-based classification. In: Proceedings of the 33rd Annual Computer Security Applications Conference, pp. 278\u2013287. ACM (2017)","DOI":"10.1145\/3134600.3134606"},{"key":"33_CR4","unstructured":"Chen, X., et al.: Variational lossy autoencoder. In: International Conference on Learning Representations (2017)"},{"key":"33_CR5","unstructured":"Dai, Z., Yang, Z., Yang, F., Cohen, W.W., Salakhutdinov, R.R.: Good semi-supervised learning that requires a bad GAN. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30, pp. 6510\u20136520. Curran Associates, Inc. (2017). http:\/\/papers.nips.cc\/paper\/7229-good-semi-supervised-learning-that-requires-a-bad-gan.pdf"},{"key":"33_CR6","unstructured":"Dinh, L., Krueger, D., Bengio, Y.: Nice: non-linear independent components estimation. In: International Conference on Learning Representations (2015)"},{"key":"33_CR7","unstructured":"Dinh, L., Sohl-Dickstein, J., Bengio, S.: Density estimation using real NVP. In: International Conference on Learning Representations (2017)"},{"key":"33_CR8","unstructured":"Dumoulin, V., et al.: Adversarially learned inference. In: International Conference on Learning Representations (2017)"},{"key":"33_CR9","unstructured":"Fawzi, A., Fawzi, H., Fawzi, O.: Adversarial vulnerability for any classifier. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 31, pp. 1178\u20131187. Curran Associates, Inc. (2018). http:\/\/papers.nips.cc\/paper\/7394-adversarial-vulnerability-for-any-classifier.pdf"},{"key":"33_CR10","doi-asserted-by":"crossref","unstructured":"Haeusser, P., Mordvintsev, A., Cremers, D.: Learning by association - a versatile semi-supervised training method for neural networks. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.74"},{"key":"33_CR11","unstructured":"Huang, C.W., Krueger, D., Lacoste, A., Courville, A.: Neural autoregressive flows. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 2078\u20132087. PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden, 10\u201315 Jul 2018. http:\/\/proceedings.mlr.press\/v80\/huang18d.html"},{"key":"33_CR12","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 448\u2013456. PMLR, Lille, France, 07\u201309 July 2015. http:\/\/proceedings.mlr.press\/v37\/ioffe15.html"},{"key":"33_CR13","unstructured":"Jackson, J., Schulman, J.: Semi-supervised learning by label gradient alignment. arXiv preprint arXiv:1902.02336 (2019)"},{"key":"33_CR14","unstructured":"Kamnitsas, K., et al.: Semi-supervised learning via compact latent space clustering. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 2459\u20132468. PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden, 10\u201315 July 2018. http:\/\/proceedings.mlr.press\/v80\/kamnitsas18a.html"},{"key":"33_CR15","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)"},{"key":"33_CR16","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: International Conference on Learning Representations (2014)"},{"key":"33_CR17","unstructured":"Kingma, D.P., Dhariwal, P.: Glow: Generative flow with invertible 1\u00a0$$\\times $$\u00a01 convolutions. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems 31, pp. 10215\u201310224. Curran Associates, Inc. (2018). http:\/\/papers.nips.cc\/paper\/8224-glow-generative-flow-with-invertible-1x1-convolutions.pdf"},{"key":"33_CR18","unstructured":"Kingma, D.P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., Welling, M.: Improved variational inference with inverse autoregressive flow. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29, pp. 4743\u20134751. Curran Associates, Inc. (2016), http:\/\/papers.nips.cc\/paper\/6581-improved-variational-inference-with-inverse-autoregressive-flow.pdf"},{"key":"33_CR19","unstructured":"Kolasinski, K.: An implementation of the GLOW paper and simple normalizing flows lib (2018). https:\/\/github.com\/kmkolasinski\/deep-learning-notes\/tree\/master\/seminars\/2018-10-Normalizing-Flows-NICE-RealNVP-GLOW"},{"key":"33_CR20","unstructured":"Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. In: International Conference on Learning Representations (2017)"},{"key":"33_CR21","unstructured":"Li, C., Xu, T., Zhu, J., Zhang, B.: Triple generative adversarial nets. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30, pp. 4088\u20134098. Curran Associates, Inc. (2017). http:\/\/papers.nips.cc\/paper\/6997-triple-generative-adversarial-nets.pdf"},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"Li, Y., Liu, S., Yang, J., Yang, M.H.: Generative face completion. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017","DOI":"10.1109\/CVPR.2017.624"},{"key":"33_CR23","doi-asserted-by":"crossref","unstructured":"Luo, Y., Zhu, J., Li, M., Ren, Y., Zhang, B.: Smooth neighbors on teacher graphs for semi-supervised learning. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018","DOI":"10.1109\/CVPR.2018.00927"},{"key":"33_CR24","unstructured":"Maas, A.L., Hannun, A.Y., Ng, A.Y.: Rectifier nonlinearities improve neural network acoustic models. In: ICML Workshop on Deep Learning for Audio, Speech and Language Processing (2013)"},{"key":"33_CR25","doi-asserted-by":"crossref","unstructured":"Miyato, T., Maeda, S.i., Koyama, M., Ishii, S.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1979\u20131993 (2018)","DOI":"10.1109\/TPAMI.2018.2858821"},{"key":"33_CR26","unstructured":"Miyato, T., Maeda, S.i., Koyama, M., Nakae, K., Ishii, S.: Distributional smoothing with virtual adversarial training. In: International Conference on Learning Representations (2016)"},{"key":"33_CR27","unstructured":"Papamakarios, G., Pavlakou, T., Murray, I.: Masked autoregressive flow for density estimation. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30, pp. 2338\u20132347. Curran Associates, Inc. (2017). http:\/\/papers.nips.cc\/paper\/6828-masked-autoregressive-flow-for-density-estimation.pdf"},{"key":"33_CR28","doi-asserted-by":"crossref","unstructured":"Park, S., Park, J., Shin, S.J., Moon, I.C.: Adversarial dropout for supervised and semi-supervised learning. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.11634"},{"key":"33_CR29","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: International Conference on Learning Representations (2016)"},{"key":"33_CR30","unstructured":"Rezende, D., Mohamed, S.: Variational inference with normalizing flows. In: Bach, F., Blei, D. (eds.) Proceedings of the 32nd International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 37, pp. 1530\u20131538. PMLR, Lille, France, 07\u201309 July 2015. http:\/\/proceedings.mlr.press\/v37\/rezende15.html"},{"key":"33_CR31","unstructured":"Sajjadi, M., Javanmardi, M., Tasdizen, T.: Regularization with stochastic transformations and perturbations for deep semi-supervised learning. In: Lee, D.D., Sugiyama, M., Luxburg, U.V., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems 29, pp. 1163\u20131171. Curran Associates, Inc. (2016). http:\/\/papers.nips.cc\/paper\/6333-regularization-with-stochastic-transformations-and-perturbations-for-deep-semi-supervised-learning.pdf"},{"key":"33_CR32","unstructured":"Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: Advances in Neural information Processing Systems, pp. 2234\u20132242 (2016)"},{"key":"33_CR33","unstructured":"Springenberg, J.T.: Unsupervised and semi-supervised learning with categorical generative adversarial networks. In: International Conference on Learning Representations (2016)"},{"key":"33_CR34","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30, pp. 1195\u20131204. Curran Associates, Inc. (2017). http:\/\/papers.nips.cc\/paper\/6719-mean-teachers-are-better-role-models-weight-averaged-consistency-targets-improve-semi-supervised-deep-learning-results.pdf"},{"key":"33_CR35","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371\u20133408 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"33_CR36","unstructured":"Xie, Q., Dai, Z., Hovy, E., Luong, M.T., Le, Q.V.: Unsupervised data augmentation for consistency training (2019)"},{"key":"33_CR37","unstructured":"Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Sch\u00f6lkopf, B.: Learning with local and global consistency. In: Thrun, S., Saul, L.K., Sch\u00f6lkopf, B. (eds.) Advances in Neural Information Processing Systems 16, pp. 321\u2013328. MIT Press (2004). http:\/\/papers.nips.cc\/paper\/2506-learning-with-local-and-global-consistency.pdf"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58452-8_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,3]],"date-time":"2024-11-03T00:17:42Z","timestamp":1730593062000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58452-8_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030584511","9783030584528"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58452-8_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"3 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1360","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"27% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}