b3b43aeeacb258365cc69cdaf42a68af-Paper.pdf

We present an approach for lifelong/continual learning of convolutional neural networks (CNN) that does not suffer from the problem of catastrophic forgetting when moving from onetask totheother. Weshowthat theactivation maps generated by the CNN trained on the old task can be calibrated using very few calibration parameters, to become relevant to the new task.