{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T06:53:06Z","timestamp":1781506386343,"version":"3.54.1"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030012243","type":"print"},{"value":"9783030012250","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\/978-3-030-01225-0_5","type":"book-chapter","created":{"date-parts":[[2018,10,8]],"date-time":"2018-10-08T08:39:54Z","timestamp":1538987994000},"page":"72-88","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":379,"title":["Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights"],"prefix":"10.1007","author":[{"given":"Arun","family":"Mallya","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dillon","family":"Davis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Svetlana","family":"Lazebnik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,10,6]]},"reference":[{"key":"5_CR1","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs\/1409.1556 (2014)"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. IJCV (2015)","DOI":"10.1007\/s11263-015-0816-y"},{"issue":"4","key":"5_CR3","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/S1364-6613(99)01294-2","volume":"3","author":"RM French","year":"1999","unstructured":"French, R.M.: Catastrophic forgetting in connectionist networks. Trends Cogn. Sci. 3(4), 128\u2013135 (1999)","journal-title":"Trends Cogn. Sci."},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Kirkpatrick, J., et al.: Overcoming catastrophic forgetting in neural networks. In: PNAS (2017)","DOI":"10.1073\/pnas.1611835114"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Rannen, A., Aljundi, R., Blaschko, M.B., Tuytelaars, T.: Encoder based lifelong learning. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.148"},{"key":"5_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1007\/978-3-319-46493-0_37","volume-title":"Computer Vision \u2013 ECCV 2016","author":"Z Li","year":"2016","unstructured":"Li, Z., Hoiem, D.: Learning without forgetting. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 614\u2013629. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_37"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Mallya, A., Lazebnik, S.: PackNet: adding multiple tasks to a single network by iterative pruning. arXiv:1711.05769 (2017)","DOI":"10.1109\/CVPR.2018.00810"},{"key":"5_CR8","unstructured":"Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The Caltech-UCSD Birds-200-2011 Dataset. Technical report CNS-TR-2011-001, California Institute of Technology (2011)"},{"key":"5_CR9","doi-asserted-by":"crossref","unstructured":"Krause, J., Stark, M., Deng, J., Fei-Fei, L.: 3D object representations for fine-grained categorization. In: CVPRW (2013)","DOI":"10.1109\/ICCVW.2013.77"},{"key":"5_CR10","doi-asserted-by":"crossref","unstructured":"Nilsback, M.E., Zisserman, A.: Automated flower classification over a large number of classes. In: ICCVGIP (2008)","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"5_CR11","unstructured":"Saleh, B., Elgammal, A.: Large-scale classification of fine-art paintings: Learning the right metric on the right feature. In: ICDMW (2015)"},{"key":"5_CR12","doi-asserted-by":"crossref","unstructured":"Eitz, M., Hays, J., Alexa, M.: How do humans sketch objects? In: SIGGRAPH (2012)","DOI":"10.1145\/2185520.2185540"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., Komodakis, N.: Wide residual networks. In: BMVC (2016)","DOI":"10.5244\/C.30.87"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"5_CR16","unstructured":"Rosenfeld, A., Tsotsos, J.K.: Incremental learning through deep adaptation. arXiv:1705.04228 (2017)"},{"key":"5_CR17","unstructured":"Rebuffi, S.A., Bilen, H., Vedaldi, A.: Learning multiple visual domains with residual adapters. In: NIPS (2017)"},{"key":"5_CR18","unstructured":"Bilen, H., Vedaldi, A.: Integrated perception with recurrent multi-task neural networks. In: NIPS (2016)"},{"key":"5_CR19","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/978-1-4615-5529-2_5","volume-title":"Learning to Learn","author":"Rich Caruana","year":"1998","unstructured":"Caruana, R.: Multitask learning. Learn. Learn (1998)"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Kokkinos, I.: Ubernet: training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.579"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Shmelkov, K., Schmid, C., Alahari, K.: Incremental learning of object detectors without catastrophic forgetting. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.368"},{"key":"5_CR22","unstructured":"Lee, S.W., Kim, J.H., Ha, J.W., Zhang, B.T.: Overcoming catastrophic forgetting by incremental moment matching. In: NIPS (2017)"},{"key":"5_CR23","unstructured":"Han, S., Pool, J., Tran, J., Dally, W.: Learning both weights and connections for efficient neural network. In: NIPS (2015)"},{"key":"5_CR24","unstructured":"Fernando, C., et al.: PathNet: evolution channels gradient descent in super neural networks. arXiv:1701.08734 (2017)"},{"key":"5_CR25","unstructured":"Rusu, A.A., et al.: Progressive neural networks. arXiv:1606.04671 (2016)"},{"key":"5_CR26","unstructured":"Courbariaux, M., Bengio, Y., David, J.P.: BinaryConnect: training deep neural networks with binary weights during propagations. In: NIPS (2015)"},{"key":"5_CR27","unstructured":"Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks. In: NIPS (2016)"},{"key":"5_CR28","unstructured":"Li, F., Zhang, B., Liu, B.: Ternary weight networks. arXiv:1605.04711 (2016)"},{"key":"5_CR29","unstructured":"Zhu, C., Han, S., Mao, H., Dally, W.J.: Trained ternary quantization. In: ICLR (2017)"},{"key":"5_CR30","unstructured":"Guo, Y., Yao, A., Chen, Y.: Dynamic network surgery for efficient DNNs. In: NIPS (2016)"},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: Places: A 10 million image database for scene recognition. TPAMI (2017)","DOI":"10.1167\/17.10.296"},{"key":"5_CR32","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"5_CR33","unstructured":"BerekeleyVision: Segmentation data splits. https:\/\/github.com\/shelhamer\/fcn.berkeleyvision.org\/tree\/master\/data\/pascal Accessed 11 Mar 2018"}],"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\/978-3-030-01225-0_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T00:05:52Z","timestamp":1665187552000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-01225-0_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030012243","9783030012250"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01225-0_5","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":"6 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"}]}}