  In class-incremental learning, the model is expected to learn new classes
continually while maintaining knowledge on previous classes. The challenge here
lies in preserving the model's ability to effectively represent prior classes
in the feature space, while adapting it to represent incoming new classes. We
propose two distillation-based objectives for class incremental learning that
leverage the structure of the feature space to maintain accuracy on previous
classes, as well as enable learning the new classes. In our first objective,
termed cross-space clustering (CSC), we propose to use the feature space
structure of the previous model to characterize directions of optimization that
maximally preserve the class: directions that all instances of a specific class
should collectively optimize towards, and those that they should collectively
optimize away from. Apart from minimizing forgetting, this indirectly
encourages the model to cluster all instances of a class in the current feature
space, and gives rise to a sense of herd-immunity, allowing all samples of a
class to jointly combat the model from forgetting the class. Our second
objective termed controlled transfer (CT) tackles incremental learning from an
understudied perspective of inter-class transfer. CT explicitly approximates
and conditions the current model on the semantic similarities between
incrementally arriving classes and prior classes. This allows the model to
learn classes in such a way that it maximizes positive forward transfer from
similar prior classes, thus increasing plasticity, and minimizes negative
backward transfer on dissimilar prior classes, whereby strengthening stability.
We perform extensive experiments on two benchmark datasets, adding our method
(CSCCT) on top of three prominent class-incremental learning methods. We
observe consistent performance improvement on a variety of experimental
settings.
