TACLE: Task and Class-Aware Exemplar-Free Semi-Supervised Class Incremental Learning

Jayateja Kalla, Rohit Kumar, Soma Biswas; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 6944-6954

We propose a novel TACLE (TAsk and CLass-awarE) framework for 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. code: https://github.com/rokmr/TACLE