Efficient Continual Learning from Sparsely Labeled Streams under Constrained Computation

A novel continual learning approach, DietCL, that efficiently utilizes both labeled and unlabeled data under a constrained computational budget to achieve superior performance compared to existing supervised and semi-supervised continual learning methods.