  In this paper, we address the incremental classifier learning problem, which
suffers from catastrophic forgetting. The main reason for catastrophic
forgetting is that the past data are not available during learning. Typical
approaches keep some exemplars for the past classes and use distillation
regularization to retain the classification capability on the past classes and
balance the past and new classes. However, there are four main problems with
these approaches. First, the loss function is not efficient for classification.
Second, there is unbalance problem between the past and new classes. Third, the
size of pre-decided exemplars is usually limited and they might not be
distinguishable from unseen new classes. Forth, the exemplars may not be
allowed to be kept for a long time due to privacy regulations. To address these
problems, we propose (a) a new loss function to combine the cross-entropy loss
and distillation loss, (b) a simple way to estimate and remove the unbalance
between the old and new classes , and (c) using Generative Adversarial Networks
(GANs) to generate historical data and select representative exemplars during
generation. We believe that the data generated by GANs have much less privacy
issues than real images because GANs do not directly copy any real image
patches. We evaluate the proposed method on CIFAR-100, Flower-102, and
MS-Celeb-1M-Base datasets and extensive experiments demonstrate the
effectiveness of our method.
