Failure Modes of Always-On Inter-Cluster Repulsion in Replay-Based Continual Learning

תקציר מקורי באנגליתarXiv:2510.07648v3 Announce Type: replace Abstract: Feature-space objectives are often added to replay-based continual learning systems with the expectation that better geometric separation will improve retention. We study a preliminary form of Cluster-Aware Replay (CAR) that combines a class-balanced replay memory with an always-active inter-cluster repulsion term (ICF). On five-task Split CIFAR-10 with a ResNet-18 backbone, the highest observed mean in a six-value sensitivity sweep reaches $22.5\pm1.4\%$ final average accuracy over three seeds, compared with $23.1\pm2.5\%$ for replay alone. ICF without replay reaches only $19.2\pm0.1\%$. All tested repulsion weights produce final accuracies between $20.1\%$ and $22.5\%$, and the detailed configuration exhibits $89.2\pm1.5$ percentage poi