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Abstract

Class-Incremental Learning (CIL) aims to learn a classification model with
the number of classes increasing phase-by-phase. The inherent problem in CIL is
the stability-plasticity dilemma between the learning of old and new classes,
i.e., high-plasticity models easily forget old classes but high-stability
models are weak to learn new classes. We alleviate this issue by proposing a
novel network architecture called Meta-Aggregating Networks (MANets) in which
we explicitly build two residual blocks at each residual level (taking ResNet
as the baseline architecture): a stable block and a plastic block. We aggregate
the output feature maps from these two blocks and then feed the results to the
next-level blocks. We meta-learn the aggregating weights in order to
dynamically optimize and balance between two types of blocks, i.e., between
stability and plasticity. We conduct extensive experiments on three CIL
benchmarks: CIFAR-100, ImageNet-Subset, and ImageNet, and show that many
existing CIL methods can be straightforwardly incorporated on the architecture
of MANets to boost their performance.