3 accepted papers
Domain adversarial adaptation in a continual setting poses significant challenges due to the limitations of accessing previous source domain data. Despite extensive research in continual learning, adversarial adaptation cannot be effectively accomplished using only a small number of stored source do…
Deep learning empowers the mainstream medical image segmentation methods. Nevertheless, current deep segmentation approaches are not capable of efficiently and effectively adapting and updating the trained models when new segmentation classes are incrementally added. In the real clinical environment…
Data-free Class-incremental Learning (CIL) is a challenging problem because rehearsing data from previous phases is strictly prohibited, causing catastrophic forgetting of Deep Neural Networks (DNNs). In this paper, we present \emph{iVoro}, a novel framework derived from computational geometry. We f…