  Although deep learning approaches have stood out in recent years due to their
state-of-the-art results, they continue to suffer from catastrophic forgetting,
a dramatic decrease in overall performance when training with new classes added
incrementally. This is due to current neural network architectures requiring
the entire dataset, consisting of all the samples from the old as well as the
new classes, to update the model -a requirement that becomes easily
unsustainable as the number of classes grows. We address this issue with our
approach to learn deep neural networks incrementally, using new data and only a
small exemplar set corresponding to samples from the old classes. This is based
on a loss composed of a distillation measure to retain the knowledge acquired
from the old classes, and a cross-entropy loss to learn the new classes. Our
incremental training is achieved while keeping the entire framework end-to-end,
i.e., learning the data representation and the classifier jointly, unlike
recent methods with no such guarantees. We evaluate our method extensively on
the CIFAR-100 and ImageNet (ILSVRC 2012) image classification datasets, and
show state-of-the-art performance.
