  We propose a novel TACLE (TAsk and CLass-awarE) framework to address the
relatively unexplored and challenging problem of exemplar-free semi-supervised
class incremental learning. In this scenario, at each new task, the model has
to learn new classes from both (few) labeled and unlabeled data without access
to exemplars from previous classes. In addition to leveraging the capabilities
of pre-trained models, TACLE proposes a novel task-adaptive threshold, thereby
maximizing the utilization of the available unlabeled data as incremental
learning progresses. Additionally, to enhance the performance of the
under-represented classes within each task, we propose a class-aware weighted
cross-entropy loss. We also exploit the unlabeled data for classifier
alignment, which further enhances the model performance. Extensive experiments
on benchmark datasets, namely CIFAR10, CIFAR100, and ImageNet-Subset100
demonstrate the effectiveness of the proposed TACLE framework. We further
showcase its effectiveness when the unlabeled data is imbalanced and also for
the extreme case of one labeled example per class.
