  This paper investigates the impact of self-supervised learning, specifically
image rotations, on various class-incremental learning paradigms. Here, each
image with a predefined rotation is considered as a new class for training. At
inference, all image rotation predictions are aggregated for the final
prediction, a strategy we term Aggregated Self-Supervision (AggSS). We observe
a shift in the deep neural network's attention towards intrinsic object
features as it learns through AggSS strategy. This learning approach
significantly enhances class-incremental learning by promoting robust feature
learning. AggSS serves as a plug-and-play module that can be seamlessly
incorporated into any class-incremental learning framework, leveraging its
powerful feature learning capabilities to enhance performance across various
class-incremental learning approaches. Extensive experiments conducted on
standard incremental learning datasets CIFAR-100 and ImageNet-Subset
demonstrate the significant role of AggSS in improving performance within these
paradigms.
