{"year":"2017","abstract":[{"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. ","lang":"eng"}],"publication_identifier":{"isbn":["978-153860457-1"]},"publisher":"IEEE","status":"public","ec_funded":1,"oa_version":"Submitted Version","department":[{"_id":"ChLa"},{"_id":"ChWo"}],"citation":{"mla":"Rebuffi, Sylvestre Alvise, et al. ICaRL: Incremental Classifier and Representation Learning. Vol. 2017, IEEE, 2017, pp. 5533–42, doi:10.1109/CVPR.2017.587.","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.","ama":"Rebuffi SA, Kolesnikov A, Sperl G, Lampert C. iCaRL: Incremental classifier and representation learning. In: Vol 2017. IEEE; 2017:5533-5542. doi:10.1109/CVPR.2017.587","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.","short":"S.A. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, in:, IEEE, 2017, pp. 5533–5542.","apa":"Rebuffi, S. A., Kolesnikov, A., Sperl, G., & 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. https://doi.org/10.1109/CVPR.2017.587","chicago":"Rebuffi, Sylvestre Alvise, Alexander Kolesnikov, Georg Sperl, and Christoph Lampert. “ICaRL: Incremental Classifier and Representation Learning,” 2017:5533–42. IEEE, 2017. https://doi.org/10.1109/CVPR.2017.587."},"type":"conference","date_created":"2018-12-11T11:49:37Z","isi":1,"article_processing_charge":"No","scopus_import":"1","title":"iCaRL: Incremental classifier and representation learning","month":"04","date_updated":"2023-09-22T09:51:58Z","oa":1,"main_file_link":[{"url":"https://arxiv.org/abs/1611.07725","open_access":"1"}],"intvolume":" 2017","quality_controlled":"1","language":[{"iso":"eng"}],"publist_id":"6400","page":"5533 - 5542","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","conference":{"start_date":"2017-07-21","name":"CVPR: Computer Vision and Pattern Recognition","end_date":"2017-07-26","location":"Honolulu, HA, United States"},"date_published":"2017-04-14T00:00:00Z","publication_status":"published","day":"14","external_id":{"isi":["000418371405066"]},"project":[{"_id":"2532554C-B435-11E9-9278-68D0E5697425","name":"Lifelong Learning of Visual Scene Understanding","grant_number":"308036","call_identifier":"FP7"}],"doi":"10.1109/CVPR.2017.587","author":[{"full_name":"Rebuffi, Sylvestre Alvise","last_name":"Rebuffi","first_name":"Sylvestre Alvise"},{"first_name":"Alexander","last_name":"Kolesnikov","full_name":"Kolesnikov, Alexander","id":"2D157DB6-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Georg","last_name":"Sperl","full_name":"Sperl, Georg","id":"4DD40360-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Christoph","last_name":"Lampert","full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887"}],"volume":2017,"_id":"998"}