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Found 4 papers, 1 papers with code
GRASP: A Rehearsal Policy for Efficient Online Continual Learning
Compared to uniform balanced sampling, GRASP achieves the same performance with 40% fewer updates.
How Efficient Are Today's Continual Learning Algorithms?
There is more to continual learning than mitigating catastrophic forgetting.
SIESTA: Efficient Online Continual Learning with Sleep
Compared to REMIND and prior arts, SIESTA is far more computationally efficient, enabling continual learning on ImageNet-1K in under 2 hours on a single GPU; moreover, in the augmentation-free setting it matches the performance of the offline learner, a milestone critical to driving adoption of continual learning in real-world applications.
Self-Supervised Training Enhances Online Continual Learning
In continual learning, a system must incrementally learn from a non-stationary data stream without catastrophic forgetting.
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