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