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