{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T06:08:38Z","timestamp":1767852518280,"version":"3.49.0"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030336752","type":"print"},{"value":"9783030336769","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-33676-9_25","type":"book-chapter","created":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T17:20:30Z","timestamp":1572024030000},"page":"360-373","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Achieving Generalizable Robustness of Deep Neural Networks by Stability Training"],"prefix":"10.1007","author":[{"given":"Jan","family":"Laermann","sequence":"first","affiliation":[]},{"given":"Wojciech","family":"Samek","sequence":"additional","affiliation":[]},{"given":"Nils","family":"Strodthoff","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,25]]},"reference":[{"key":"25_CR1","first-page":"3365","volume-title":"NIPS","author":"P Bachman","year":"2014","unstructured":"Bachman, P., Alsharif, O., Precup, D.: Learning with pseudo-ensembles. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) NIPS, pp. 3365\u20133373. Curran Associates, Inc., New York (2014)"},{"key":"25_CR2","unstructured":"Berthelot, D., Carlini, N., Goodfellow, I., Papernot, N., Oliver, A., Raffel, C.: MixMatch: a holistic approach to semi-supervised learning (2019)"},{"key":"25_CR3","unstructured":"Carlini, N., et al.: On evaluating adversarial robustness. arXiv preprint: arXiv:1902.06705 (2019)"},{"key":"25_CR4","volume-title":"Semi-Supervised Learning","author":"O Chapelle","year":"2010","unstructured":"Chapelle, O., Schlkopf, B., Zien, A.: Semi-Supervised Learning. The MIT Press, Cambridge (2010)"},{"key":"25_CR5","doi-asserted-by":"crossref","unstructured":"Cubuk, E.D., Zoph, B., Mane, D., Vasudevan, V., Le, Q.V.: AutoAugment: learning augmentation policies from data. arXiv preprint: arXiv:1805.09501 (2018)","DOI":"10.1109\/CVPR.2019.00020"},{"issue":"5","key":"25_CR6","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1109\/TNNLS.2013.2292894","volume":"25","author":"B Fr\u00e9nay","year":"2014","unstructured":"Fr\u00e9nay, B., Verleysen, M.: Classification in the presence of label noise: a survey. IEEE Trans. Neural Netw. Learn. Syst. 25(5), 845\u2013869 (2014)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"4","key":"25_CR7","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/S1364-6613(99)01294-2","volume":"3","author":"RM French","year":"1994","unstructured":"French, R.M.: Catastrophic forgetting in connectionist networks. Trends Cogn. Sci. 3(4), 128\u2013135 (1994)","journal-title":"Trends Cogn. Sci."},{"key":"25_CR8","unstructured":"Gal, Y.: Uncertainty in deep learning. Ph.D. thesis, University of Cambridge (2016)"},{"key":"25_CR9","unstructured":"Goodfellow, I., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. In: International Conference on Learning Representations (2015)"},{"key":"25_CR10","unstructured":"Hataya, R., Nakayama, H.: Unifying semi-supervised and robust learning by mixup. In: ICLR the 2nd Learning from Limited Labeled Data (LLD) Workshop (2019)"},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: CVPR, pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"25_CR13","unstructured":"Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML, pp. 448\u2013456 (2015)"},{"key":"25_CR14","first-page":"1097","volume-title":"NIPS","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Pereira, F., Burges, C., Bottou, L., Weinberger, K. (eds.) NIPS, pp. 1097\u20131105. Curran Associates, Inc., New York (2012)"},{"key":"25_CR15","unstructured":"Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. In: ICLR (2017)"},{"issue":"8","key":"25_CR16","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1109\/TPAMI.2018.2858821","volume":"41","author":"T Miyato","year":"2018","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)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"25_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dsp.2017.10.011","volume":"73","author":"G Montavon","year":"2018","unstructured":"Montavon, G., Samek, W., M\u00fcller, K.R.: Methods for interpreting and understanding deep neural networks. Digit. Signal Process. 73, 1\u201315 (2018)","journal-title":"Digit. Signal Process."},{"key":"25_CR18","first-page":"543","volume":"269","author":"Y Nesterov","year":"1983","unstructured":"Nesterov, Y.: A method for unconstrained convex minimization problem with the rate of convergence O(1\/k $$^2$$ ). Dokl. AN USSR 269, 543\u2013547 (1983)","journal-title":"Dokl. AN USSR"},{"key":"25_CR19","unstructured":"Oliver, A., Odena, A., Raffel, C., Cubuk, E.D., Goodfellow, I.J.: Realistic evaluation of deep semi-supervised learning algorithms. arXiv preprint: arXiv:1804.09170 (2018)"},{"key":"25_CR20","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch. In: NIPS Autodiff Workshop (2017)"},{"key":"25_CR21","unstructured":"Rajput, S., Feng, Z., Charles, Z., Loh, P.L., Papailiopoulos, D.: Does data augmentation lead to positive margin? arXiv preprint: arXiv:1905.03177 (2019)"},{"key":"25_CR22","unstructured":"Sajjadi, M., Javanmardi, M., Tasdizen, T.: Regularization with stochastic transformations and perturbations for deep semi-supervised learning. In: NIPS, pp. 1171\u20131179 (2016)"},{"issue":"7587","key":"25_CR23","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1038\/nature16961","volume":"529","author":"D Silver","year":"2016","unstructured":"Silver, D., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484\u2013489 (2016)","journal-title":"Nature"},{"key":"25_CR24","unstructured":"Stanford University\u2019s CS231 course: Tiny ImageNet. https:\/\/tiny-imagenet.herokuapp.com\/ . Accessed 7 May 2019"},{"key":"25_CR25","unstructured":"Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint: arXiv:1312.6199 (2013)"},{"key":"25_CR26","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: NIPS (2017)"},{"key":"25_CR27","doi-asserted-by":"crossref","unstructured":"Taylor, L., Nitschke, G.: Improving deep learning using generic data augmentation. arXiv preprint: arXiv:1708.06020 (2017)","DOI":"10.1109\/SSCI.2018.8628742"},{"key":"25_CR28","doi-asserted-by":"crossref","unstructured":"Verma, V., Lamb, A., Kannala, J., Bengio, Y., Lopez-Paz, D.: Interpolation consistency training for semi-supervised learning. arXiv preprint: arXiv:1903.03825 (2019)","DOI":"10.24963\/ijcai.2019\/504"},{"key":"25_CR29","unstructured":"Xie, Q., Dai, Z., Hovy, E., Luong, M.T., Le, Q.V.: Unsupervised data augmentation. arXiv preprint: arXiv:1904.12848 (2019)"},{"key":"25_CR30","first-page":"807","volume-title":"NIPS","author":"LS Yaeger","year":"1997","unstructured":"Yaeger, L.S., Lyon, R.F., Webb, B.J.: Effective training of a neural network character classifier for word recognition. In: Mozer, M.C., Jordan, M.I., Petsche, T. (eds.) NIPS, pp. 807\u2013816. MIT Press, Cambridge (1997)"},{"key":"25_CR31","unstructured":"Zhang, C., Cui, J., Yang, B.: Learning optimal data augmentation policies via Bayesian optimization for image classification tasks (2019)"},{"key":"25_CR32","unstructured":"Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. In: ICLR (2018)"},{"key":"25_CR33","doi-asserted-by":"crossref","unstructured":"Zheng, S., Song, Y., Leung, T., Goodfellow, I.: Improving the robustness of deep neural networks via stability training. In: CVPR, pp. 4480\u20134488 (2016)","DOI":"10.1109\/CVPR.2016.485"},{"key":"25_CR34","unstructured":"Zhu, X.J.: Semi-supervised learning literature survey. Technical report, University of Wisconsin-Madison Department of Computer Sciences (2005)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-33676-9_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,26]],"date-time":"2021-01-26T03:20:36Z","timestamp":1611631236000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-33676-9_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030336752","9783030336769"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-33676-9_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"25 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DAGM GCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"German Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dortmund","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"41","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dagm2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/gcpr2019.tu-dortmund.de\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"91","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":"43","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":"47% - 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":"5","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)"}}]}}