{"status":"public","publist_id":"6400","type":"conference","department":[{"_id":"ChLa"},{"_id":"ChWo"}],"page":"5533 - 5542","citation":{"chicago":"Rebuffi, Sylvestre Alvise, Alexander Kolesnikov, Georg Sperl, and Christoph Lampert. “ICaRL: Incremental Classifier and Representation Learning,” 2017:5533–42. IEEE, 2017. <a href=\"https://doi.org/10.1109/CVPR.2017.587\">https://doi.org/10.1109/CVPR.2017.587</a>.","short":"S.A. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, in:, IEEE, 2017, pp. 5533–5542.","apa":"Rebuffi, S. A., Kolesnikov, A., Sperl, G., &#38; Lampert, C. (2017). iCaRL: Incremental classifier and representation learning (Vol. 2017, pp. 5533–5542). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. <a href=\"https://doi.org/10.1109/CVPR.2017.587\">https://doi.org/10.1109/CVPR.2017.587</a>","ista":"Rebuffi SA, Kolesnikov A, Sperl G, Lampert C. 2017. iCaRL: Incremental classifier and representation learning. CVPR: Computer Vision and Pattern Recognition vol. 2017, 5533–5542.","ama":"Rebuffi SA, Kolesnikov A, Sperl G, Lampert C. iCaRL: Incremental classifier and representation learning. In: Vol 2017. IEEE; 2017:5533-5542. doi:<a href=\"https://doi.org/10.1109/CVPR.2017.587\">10.1109/CVPR.2017.587</a>","ieee":"S. A. Rebuffi, A. Kolesnikov, G. Sperl, and C. Lampert, “iCaRL: Incremental classifier and representation learning,” presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 5533–5542.","mla":"Rebuffi, Sylvestre Alvise, et al. <i>ICaRL: Incremental Classifier and Representation Learning</i>. Vol. 2017, IEEE, 2017, pp. 5533–42, doi:<a href=\"https://doi.org/10.1109/CVPR.2017.587\">10.1109/CVPR.2017.587</a>."},"scopus_import":"1","volume":2017,"project":[{"name":"Lifelong Learning of Visual Scene Understanding","_id":"2532554C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"308036"}],"oa":1,"_id":"998","doi":"10.1109/CVPR.2017.587","author":[{"full_name":"Rebuffi, Sylvestre Alvise","first_name":"Sylvestre Alvise","last_name":"Rebuffi"},{"full_name":"Kolesnikov, Alexander","first_name":"Alexander","id":"2D157DB6-F248-11E8-B48F-1D18A9856A87","last_name":"Kolesnikov"},{"last_name":"Sperl","id":"4DD40360-F248-11E8-B48F-1D18A9856A87","first_name":"Georg","full_name":"Sperl, Georg"},{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","last_name":"Lampert","full_name":"Lampert, Christoph","first_name":"Christoph"}],"day":"14","article_processing_charge":"No","month":"04","publication_identifier":{"isbn":["978-153860457-1"]},"oa_version":"Submitted Version","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1611.07725"}],"year":"2017","external_id":{"isi":["000418371405066"]},"language":[{"iso":"eng"}],"date_published":"2017-04-14T00:00:00Z","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","date_created":"2018-12-11T11:49:37Z","date_updated":"2023-09-22T09:51:58Z","isi":1,"ec_funded":1,"conference":{"end_date":"2017-07-26","start_date":"2017-07-21","name":"CVPR: Computer Vision and Pattern Recognition","location":"Honolulu, HA, United States"},"quality_controlled":"1","abstract":[{"lang":"eng","text":"A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data. In this work, we introduce a new training strategy, iCaRL, that allows learning in such a class-incremental way: only the training data for a small number of classes has to be present at the same time and new classes can be added progressively. iCaRL learns strong classifiers and a data representation simultaneously. This distinguishes it from earlier works that were fundamentally limited to fixed data representations and therefore incompatible with deep learning architectures. We show by experiments on CIFAR-100 and ImageNet ILSVRC 2012 data that iCaRL can learn many classes incrementally over a long period of time where other strategies quickly fail. "}],"title":"iCaRL: Incremental classifier and representation learning","publisher":"IEEE","intvolume":"      2017","publication_status":"published"}