  This work investigates the entanglement between Continual Learning (CL) and
Transfer Learning (TL). In particular, we shed light on the widespread
application of network pretraining, highlighting that it is itself subject to
catastrophic forgetting. Unfortunately, this issue leads to the
under-exploitation of knowledge transfer during later tasks. On this ground, we
propose Transfer without Forgetting (TwF), a hybrid approach building upon a
fixed pretrained sibling network, which continuously propagates the knowledge
inherent in the source domain through a layer-wise loss term. Our experiments
indicate that TwF steadily outperforms other CL methods across a variety of
settings, averaging a 4.81% gain in Class-Incremental accuracy over a variety
of datasets and different buffer sizes.
