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 classincremental 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.