  Catastrophic forgetting is a problem of neural networks that loses the
information of the first task after training the second task. Here, we propose
a method, i.e. incremental moment matching (IMM), to resolve this problem. IMM
incrementally matches the moment of the posterior distribution of the neural
network which is trained on the first and the second task, respectively. To
make the search space of posterior parameter smooth, the IMM procedure is
complemented by various transfer learning techniques including weight transfer,
L2-norm of the old and the new parameter, and a variant of dropout with the old
parameter. We analyze our approach on a variety of datasets including the
MNIST, CIFAR-10, Caltech-UCSD-Birds, and Lifelog datasets. The experimental
results show that IMM achieves state-of-the-art performance by balancing the
information between an old and a new network.
