---
res:
  bibo_abstract:
  - '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. @eng'
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Sylvestre Alvise
      foaf_name: Rebuffi, Sylvestre Alvise
      foaf_surname: Rebuffi
  - foaf_Person:
      foaf_givenName: Alexander
      foaf_name: Kolesnikov, Alexander
      foaf_surname: Kolesnikov
      foaf_workInfoHomepage: http://www.librecat.org/personId=2D157DB6-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Georg
      foaf_name: Sperl, Georg
      foaf_surname: Sperl
      foaf_workInfoHomepage: http://www.librecat.org/personId=4DD40360-F248-11E8-B48F-1D18A9856A87
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Lampert, Christoph
      foaf_surname: Lampert
      foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8622-7887
  bibo_doi: 10.1109/CVPR.2017.587
  bibo_volume: 2017
  dct_date: 2017^xs_gYear
  dct_identifier:
  - UT:000418371405066
  dct_isPartOf:
  - http://id.crossref.org/issn/978-153860457-1
  dct_language: eng
  dct_publisher: IEEE@
  dct_title: 'iCaRL: Incremental classifier and representation learning@'
...