  We propose a novel continual learning method called Residual Continual
Learning (ResCL). Our method can prevent the catastrophic forgetting phenomenon
in sequential learning of multiple tasks, without any source task information
except the original network. ResCL reparameterizes network parameters by
linearly combining each layer of the original network and a fine-tuned network;
therefore, the size of the network does not increase at all. To apply the
proposed method to general convolutional neural networks, the effects of batch
normalization layers are also considered. By utilizing residual-learning-like
reparameterization and a special weight decay loss, the trade-off between
source and target performance is effectively controlled. The proposed method
exhibits state-of-the-art performance in various continual learning scenarios.
