  Incremental learning targets at achieving good performance on new categories
without forgetting old ones. Knowledge distillation has been shown critical in
preserving the performance on old classes. Conventional methods, however,
sequentially distill knowledge only from the last model, leading to performance
degradation on the old classes in later incremental learning steps. In this
paper, we propose a multi-model and multi-level knowledge distillation
strategy. Instead of sequentially distilling knowledge only from the last
model, we directly leverage all previous model snapshots. In addition, we
incorporate an auxiliary distillation to further preserve knowledge encoded at
the intermediate feature levels. To make the model more memory efficient, we
adapt mask based pruning to reconstruct all previous models with a small memory
footprint. Experiments on standard incremental learning benchmarks show that
our method preserves the knowledge on old classes better and improves the
overall performance over standard distillation techniques.
