Siddeshwar Raghavan

PANDA – Patch and Distribution-Aware Augmentation for Long-Tailed Exemplar-Free Continual Learning

Exemplar-Free Continual Learning (EFCL) restricts the storage of previous task data and is highly susceptible to catastrophic forgetting. While pre-trained models (PTMs) are increasingly leveraged for EFCL, existing methods often overlook the inherent imbalance of real-world data distributions. We d