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iCaRL: Incremental Classifier and Representation Learning (CVPR'2017)

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.

Citation

@inproceedings{rebuffi2017icarl,
  title={icarl: Incremental classifier and representation learning},
  author={Rebuffi, Sylvestre-Alvise and Kolesnikov, Alexander and Sperl, Georg and Lampert, Christoph H},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR)},
  pages={2001--2010},
  year={2017}
}

How to Reproduce iCaRL

  • Step1: Set the path in run_trainer.py with ./config/icarl.yaml 
    config = Config("./config/icarl.yaml").get_config_dict()
  • Step2: Run command 
    python run_trainer.py

Results on CIFAR100 dataset

Dataset Num of Tasks Buffer Size Reproduced Accuracy
CIFAR100 2 2000 62.4
CIFAR100 5 2000 54.4
CIFAR100 10 2000 46.5