  The ability to learn from incrementally arriving data is essential for any
life-long learning system. However, standard deep neural networks forget the
knowledge about the old tasks, a phenomenon called catastrophic forgetting,
when trained on incrementally arriving data. We discuss the biases in current
Generative Adversarial Networks (GAN) based approaches that learn the
classifier by knowledge distillation from previously trained classifiers. These
biases cause the trained classifier to perform poorly. We propose an approach
to remove these biases by distilling knowledge from the classifier of AC-GAN.
Experiments on MNIST and CIFAR10 show that this method is comparable to current
state of the art rehearsal based approaches. The code for this paper is
available at https://bit.ly/incremental-learning
