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On the Convergence of Continual Learning with Adaptive Methods
One of the objectives of continual learning is to prevent catastrophic forgetting in learning multiple tasks sequentially, and the existing solutions have been driven by the conceptualization of the plasticity-stability dilemma.
Learning to Learn Unlearned Feature for Brain Tumor Segmentation
We propose a fine-tuning algorithm for brain tumor segmentation that needs only a few data samples and helps networks not to forget the original tasks.
On the Convergence of Nonconvex Continual Learning with Adaptive Learning Rate
The memory based continual learning stores a small subset of the data for previous tasks and applies various methods such as quadratic programming and sample selection.
Nonconvex Continual Learning with Episodic Memory
We also show that memory-based approaches have an inherent problem of overfitting to memory, which degrades the performance on previously learned tasks, namely catastrophic forgetting.
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