SPrint: Self-Paced Continual Learning with Adaptive Curriculum and Memory Replay

Continual learning aims to progressively acquire new knowledge while retaining previously learned information, addressing the challenge of catastrophic forgetting. This paper introduces a novel continual learning method, called SPrint, which is the first research effort to devise the principles of self-paced learning for solving continual learning problems. SPrint dynamically adapts the complexity of samples for both new and previous tasks in response to the model's current learning capacity. It employs a self-paced loss function for sampling new tasks and a forgetting occurrence for sampling previous tasks with replay memory. Through extensive empirical evaluation, we demonstrate that SPrint consistently outperforms state-of-the-art methods in various continual learning benchmarks. Our source code is publicly available at https://github.com/bigbases/SPrint.