{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T19:53:18Z","timestamp":1775245998684,"version":"3.50.1"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030012151","type":"print"},{"value":"9783030012168","type":"electronic"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-01216-8_10","type":"book-chapter","created":{"date-parts":[[2018,10,8]],"date-time":"2018-10-08T11:10:26Z","timestamp":1538997026000},"page":"152-166","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["SaaS: Speed as a Supervisor for Semi-supervised Learning"],"prefix":"10.1007","author":[{"given":"Safa","family":"Cicek","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alhussein","family":"Fawzi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefano","family":"Soatto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,10,9]]},"reference":[{"issue":"3","key":"10_CR1","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1109\/TNN.2009.2015974","volume":"20","author":"O Chapelle","year":"2009","unstructured":"Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised learning (Chapelle, O., et al. (eds.) 2006) [book reviews]. IEEE Trans. Neural Netw. 20(3), 542 (2009)","journal-title":"IEEE Trans. Neural Netw."},{"key":"10_CR2","unstructured":"Grandvalet, Y., Bengio, Y.: Semi-supervised learning by entropy minimization. In: Advances in Neural Information Processing Systems, pp. 529\u2013536 (2005)"},{"key":"10_CR3","unstructured":"Miyato, T., Maeda, S.I., Koyama, M., Ishii, S.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. arXiv preprint arXiv:1704.03976 (2017)"},{"key":"10_CR4","unstructured":"Dai, Z., Yang, Z., Yang, F., Cohen, W.W., Salakhutdinov, R.R.: Good semi-supervised learning that requires a bad gan. In: Advances in Neural Information Processing Systems, pp. 6513\u20136523 (2017)"},{"key":"10_CR5","unstructured":"Krause, A., Perona, P., Gomes, R.G.: Discriminative clustering by regularized information maximization. In: Advances in Neural Information Processing Systems, pp. 775\u2013783 (2010)"},{"key":"10_CR6","unstructured":"Springenberg, J.T.: Unsupervised and semi-supervised learning with categorical generative adversarial networks. arXiv preprint arXiv:1511.06390 (2015)"},{"key":"10_CR7","doi-asserted-by":"crossref","unstructured":"Sajjadi, M., Javanmardi, M., Tasdizen, T.: Mutual exclusivity loss for semi-supervised deep learning. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 1908\u20131912. IEEE (2016)","DOI":"10.1109\/ICIP.2016.7532690"},{"key":"10_CR8","unstructured":"Xu, J., Zhang, Z., Friedman, T., Liang, Y., Van den Broeck, G.: A semantic loss function for deep learning with symbolic knowledge. arXiv preprint arXiv:1711.11157 (2017)"},{"key":"10_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1007\/978-3-642-33712-3_27","volume-title":"Computer Vision \u2013 ECCV 2012","author":"A Shrivastava","year":"2012","unstructured":"Shrivastava, A., Singh, S., Gupta, A.: Constrained semi-supervised learning using attributes and comparative attributes. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7574, pp. 369\u2013383. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33712-3_27"},{"key":"10_CR10","unstructured":"Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O.: Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530 (2016)"},{"key":"10_CR11","unstructured":"Pereyra, G., Tucker, G., Chorowski, J., Kaiser, \u0141., Hinton, G.: Regularizing neural networks by penalizing confident output distributions. arXiv preprint arXiv:1701.06548 (2017)"},{"key":"10_CR12","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"10_CR13","unstructured":"Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: NIPS Workshop on Deep Learning and Unsupervised Feature Learning, vol. 2011, p. 5 (2011)"},{"key":"10_CR14","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images (2009)"},{"key":"10_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1007\/978-3-319-46493-0_38","volume-title":"Computer Vision \u2013 ECCV 2016","author":"K He","year":"2016","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630\u2013645. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_38"},{"key":"10_CR16","unstructured":"Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: Advances in Neural Information Processing Systems, pp. 1195\u20131204 (2017)"},{"key":"10_CR17","unstructured":"Laine, S., Aila, T.: Temporal ensembling for semi-supervised learning. arXiv preprint arXiv:1610.02242 (2016)"},{"key":"10_CR18","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":"10_CR19","unstructured":"Sajjadi, M., Javanmardi, M., Tasdizen, T.: Regularization with stochastic transformations and perturbations for deep semi-supervised learning. In: Advances in Neural Information Processing Systems, pp. 1163\u20131171 (2016)"},{"key":"10_CR20","unstructured":"Keskar, N.S., Mudigere, D., Nocedal, J., Smelyanskiy, M., Tang, P.T.P.: On large-batch training for deep learning: generalization gap and sharp minima. arXiv preprint arXiv:1609.04836 (2016)"},{"key":"10_CR21","unstructured":"Welling, M., Teh, Y.W.: Bayesian learning via stochastic gradient langevin dynamics. In: Proceedings of the 28th International Conference on Machine Learning (ICML-11), pp. 681\u2013688 (2011)"},{"key":"10_CR22","unstructured":"Raginsky, M., Rakhlin, A., Telgarsky, M.: Non-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis. In: Proceedings of the 30th Conference on Learning Theory, COLT 2017, Amsterdam, The Netherlands, July 7\u201310 2017, pp. 1674\u20131703 (2017)"},{"key":"10_CR23","unstructured":"Chaudhari, P., Choromanska, A., Soatto, S., LeCun, Y.: Entropy-SGD: biasing gradient descent into wide valleys. arXiv preprint arXiv:1611.01838 (2016)"},{"key":"10_CR24","unstructured":"Hardt, M., Recht, B., Singer, Y.: Train faster, generalize better: Stability of stochastic gradient descent. In: Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19\u201324 2016, pp. 1225\u20131234 (2016)"},{"key":"10_CR25","unstructured":"Miyato, T., Maeda, S.i., Koyama, M., Nakae, K., Ishii, S.: Distributional smoothing with virtual adversarial training. arXiv preprint arXiv:1507.00677 (2015)"},{"key":"10_CR26","unstructured":"Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on Computational learning theory, pp. 92\u2013100. ACM (1998)","DOI":"10.1145\/279943.279962"},{"key":"10_CR28","unstructured":"Simard, P., Victorri, B., LeCun, Y., Denker, J.: Tangent prop-a formalism for specifying selected invariances in an adaptive network. In: Advances in Neural Information Processing Systems, pp. 895\u2013903 (1992)"},{"key":"10_CR29","unstructured":"Dumoulin, V., et al.: Adversarially learned inference. arXiv preprint arXiv:1606.00704 (2016)"},{"key":"10_CR30","unstructured":"Yang, Z., Cohen, W.W., Salakhutdinov, R.: Revisiting semi-supervised learning with graph embeddings. In: Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19\u201324 2016, pp. 40\u201348 (2016)"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Nie, F., Wang, H., Huang, H., Ding, C.: Unsupervised and semi-supervised learning via 1-norm graph. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2268\u20132273. IEEE (2011)","DOI":"10.1109\/ICCV.2011.6126506"},{"key":"10_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1007\/978-3-319-46484-8_35","volume-title":"Computer Vision \u2013 ECCV 2016","author":"H Su","year":"2016","unstructured":"Su, H., Zhu, J., Yin, Z., Dong, Y., Zhang, B.: Efficient and robust semi-supervised learning over a sparse-regularized graph. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 583\u2013598. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_35"},{"key":"10_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-15567-3_1","volume-title":"Computer Vision \u2013 ECCV 2010","author":"Z Lu","year":"2010","unstructured":"Lu, Z., Ip, H.H.S.: Constrained Spectral clustering via exhaustive and efficient constraint propagation. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6316, pp. 1\u201314. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-15567-3_1"},{"key":"10_CR34","doi-asserted-by":"crossref","unstructured":"Li, C.G., Lin, Z., Zhang, H., Guo, J.: Learning semi-supervised representation towards a unified optimization framework for semi-supervised learning. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2767\u20132775 (2015)","DOI":"10.1109\/ICCV.2015.317"},{"key":"10_CR35","doi-asserted-by":"crossref","unstructured":"Wang, X., Guo, X., Li, S.Z.: Adaptively unified semi-supervised dictionary learning with active points. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1787\u20131795 (2015)","DOI":"10.1109\/ICCV.2015.208"},{"key":"10_CR36","doi-asserted-by":"crossref","unstructured":"Haeusser, P., Mordvintsev, A., Cremers, D.: Learning by association-a versatile semi-supervised training method for neural networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.74"},{"key":"10_CR37","unstructured":"Gaunt, A., Tarlow, D., Brockschmidt, M., Urtasun, R., Liao, R., Zemel, R.: Graph partition neural networks for semi-supervised classification (2018)"},{"key":"10_CR38","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"10_CR39","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"639","DOI":"10.1007\/978-3-642-35289-8_34","volume-title":"Neural Networks: Tricks of the Trade","author":"J Weston","year":"2012","unstructured":"Weston, J., Ratle, F., Mobahi, H., Collobert, R.: Deep learning via semi-supervised embedding. In: Montavon, G., Orr, G.B., M\u00fcller, K.-R. (eds.) Neural Networks: Tricks of the Trade. LNCS, vol. 7700, pp. 639\u2013655. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-35289-8_34"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01216-8_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T18:57:37Z","timestamp":1775242657000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01216-8_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030012151","9783030012168"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01216-8_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"9 October 2018","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":"Munich","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":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2018.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}