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Found 3 papers, 2 papers with code
FOCIL: Finetune-and-Freeze for Online Class Incremental Learning by Training Randomly Pruned Sparse Experts
Class incremental learning (CIL) in an online continual learning setting strives to acquire knowledge on a series of novel classes from a data stream, using each data point only once for training.
Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates
Therefore, we perform a comprehensive study in which we investigate various DST components to find the best topology per task on well-known CIFAR100 and miniImageNet benchmarks in a task-incremental CL setup since our primary focus is to evaluate the performance of various DST criteria, rather than the process of mask selection.
Adaptive Regularization for Class-Incremental Learning
Class-Incremental Learning updates a deep classifier with new categories while maintaining the previously observed class accuracy.
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