{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T08:03:49Z","timestamp":1726041829347},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030298937"},{"type":"electronic","value":"9783030298944"}],"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\/978-3-030-29894-4_45","type":"book-chapter","created":{"date-parts":[[2019,8,22]],"date-time":"2019-08-22T21:41:59Z","timestamp":1566510119000},"page":"555-567","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Effective Data Augmentations via Unbiased GAN Utilization"],"prefix":"10.1007","author":[{"given":"Sunny","family":"Verma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liming","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,8,23]]},"reference":[{"key":"45_CR1","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, 06\u201311 August 2017, vol. 70, pp. 214\u2013223. PMLR, International Convention Centre, Sydney, Australia (2017)"},{"key":"45_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1007\/3-540-45681-3_6","volume-title":"Principles of Data Mining and Knowledge Discovery","author":"D Brain","year":"2002","unstructured":"Brain, D., Webb, G.I.: The need for low bias algorithms in classification learning from large data sets. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS, vol. 2431, pp. 62\u201373. Springer, Heidelberg (2002). https:\/\/doi.org\/10.1007\/3-540-45681-3_6"},{"key":"45_CR3","unstructured":"Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., Abbeel, P.: InfoGAN: interpretable representation learning by information maximizing generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2172\u20132180 (2016)"},{"key":"45_CR4","unstructured":"Coates, A., Ng, A., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, pp. 215\u2013223 (2011)"},{"key":"45_CR5","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"key":"45_CR6","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of Wasserstein GANs. In: NIPS (2017)"},{"key":"45_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1007\/978-3-642-33718-5_12","volume-title":"Computer Vision \u2013 ECCV 2012","author":"A Khosla","year":"2012","unstructured":"Khosla, A., Zhou, T., Malisiewicz, T., Efros, A.A., Torralba, A.: Undoing the damage of dataset bias. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 158\u2013171. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33718-5_12"},{"key":"45_CR8","doi-asserted-by":"crossref","unstructured":"Kleinberg, J., Mullainathan, S., Raghavan, M.: Inherent trade-offs in the fair determination of risk scores. In: ITCS (2017)","DOI":"10.1145\/3219617.3219634"},{"key":"45_CR9","unstructured":"Kohavi, R., Wolpert, D.H., et al.: Bias plus variance decomposition for zero-one loss functions. In: Machine Learning, Proceedings of the Thirteenth International Conference (ICML), pp. 275\u2013283 (1996)"},{"key":"45_CR10","doi-asserted-by":"crossref","unstructured":"Kohli, N., Yadav, D., Vatsa, M., Singh, R., Noore, A.: Synthetic iris presentation attack using iDCGAN. In: IJCB (2017)","DOI":"10.1109\/BTAS.2017.8272756"},{"key":"45_CR11","unstructured":"Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Department of Computer Science, University of Toronto (2009)"},{"issue":"2","key":"45_CR12","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1023\/A:1022859003006","volume":"51","author":"LI Kuncheva","year":"2003","unstructured":"Kuncheva, L.I., Whitaker, C.J.: Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach. Learn. 51(2), 181\u2013207 (2003)","journal-title":"Mach. Learn."},{"key":"45_CR13","unstructured":"Liu, M.Y., Tuzel, O.: Coupled generative adversarial networks. In: Advances in Neural Information Processing Systems, pp. 469\u2013477 (2016)"},{"key":"45_CR14","doi-asserted-by":"crossref","unstructured":"McLaughlin, N., Del Rincon, J.M., Miller, P.: Data-augmentation for reducing dataset bias in person re-identification. In: 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE (2015)","DOI":"10.1109\/AVSS.2015.7301739"},{"key":"45_CR15","doi-asserted-by":"crossref","unstructured":"Paulin, M., Revaud, J., Harchaoui, Z., Perronnin, F., Schmid, C.: Transformation pursuit for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3646\u20133653 (2014)","DOI":"10.1109\/CVPR.2014.466"},{"key":"45_CR16","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: ICLR (2016)"},{"key":"45_CR17","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":"45_CR18","unstructured":"Sato, I., Nishimura, H., Yokoi, K.: APAC: augmented pattern classification with neural networks. arXiv preprint arXiv:1505.03229 (2015)"},{"key":"45_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"218","DOI":"10.1007\/978-3-030-01216-8_14","volume-title":"Computer Vision \u2013 ECCV 2018","author":"K Shmelkov","year":"2018","unstructured":"Shmelkov, K., Schmid, C., Alahari, K.: How good is my GAN? In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 218\u2013234. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01216-8_14"},{"key":"45_CR20","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., Webb, R.: Learning from simulated and unsupervised images through adversarial training. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2242\u20132251 (2017)","DOI":"10.1109\/CVPR.2017.241"},{"key":"45_CR21","doi-asserted-by":"crossref","unstructured":"Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20139 (2015)","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"45_CR22","series-title":"Advances in Computer Vision and Pattern Recognition","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/978-3-319-58347-1_2","volume-title":"Domain Adaptation in Computer Vision Applications","author":"T Tommasi","year":"2017","unstructured":"Tommasi, T., Patricia, N., Caputo, B., Tuytelaars, T.: A deeper look at dataset bias. In: Csurka, G. (ed.) Domain Adaptation in Computer Vision Applications. ACVPR, pp. 37\u201355. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-58347-1_2"},{"key":"45_CR23","doi-asserted-by":"crossref","unstructured":"Torralba, A., Efros, A.A.: Unbiased look at dataset bias. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2011)","DOI":"10.1109\/CVPR.2011.5995347"},{"key":"45_CR24","doi-asserted-by":"crossref","unstructured":"Xian, Y., Lorenz, T., Schiele, B., Akata, Z.: Feature generating networks for zero-shot learning. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00581"},{"key":"45_CR25","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.244"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2019: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-29894-4_45","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,26]],"date-time":"2022-09-26T04:41:30Z","timestamp":1664167290000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-29894-4_45"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030298937","9783030298944"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-29894-4_45","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"23 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cuvu, Yanuka Island","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Fiji","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":"26 August 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2019","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":"pricai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}