@inproceedings{998,
  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. },
  author       = {Rebuffi, Sylvestre Alvise and Kolesnikov, Alexander and Sperl, Georg and Lampert, Christoph},
  isbn         = {978-153860457-1},
  location     = {Honolulu, HA, United States},
  pages        = {5533 -- 5542},
  publisher    = {IEEE},
  title        = {{iCaRL: Incremental classifier and representation learning}},
  doi          = {10.1109/CVPR.2017.587},
  volume       = {2017},
  year         = {2017},
}