  Class-incremental learning (CIL) has been widely studied under the setting of
starting from a small number of classes (base classes). Instead, we explore an
understudied real-world setting of CIL that starts with a strong model
pre-trained on a large number of base classes. We hypothesize that a strong
base model can provide a good representation for novel classes and incremental
learning can be done with small adaptations. We propose a 2-stage training
scheme, i) feature augmentation -- cloning part of the backbone and fine-tuning
it on the novel data, and ii) fusion -- combining the base and novel
classifiers into a unified classifier. Experiments show that the proposed
method significantly outperforms state-of-the-art CIL methods on the
large-scale ImageNet dataset (e.g. +10% overall accuracy than the best). We
also propose and analyze understudied practical CIL scenarios, such as
base-novel overlap with distribution shift. Our proposed method is robust and
generalizes to all analyzed CIL settings.
  Code is available at https://github.com/amazon-research/sp-cil.
