论文标题
梯度情节记忆具有持续学习的软限制
Gradient Episodic Memory with a Soft Constraint for Continual Learning
论文作者
论文摘要
持续学习中的灾难性遗忘是基于梯度的神经网络中的一种常见破坏性现象，这些现象是学习顺序任务的，这与忘记人类可以学习和积累一生的知识有很大不同。灾难性的遗忘是模型正在学习新任务时，在先前任务上的性能大大降低的致命缺点。为了减轻这个问题，该模型应具有学习新知识并保留学习知识的能力。我们提出了一个平均梯度情节记忆（A-GEM），其中具有软约束$ε\在[0，1] $中，这是学习新知识和保留学习知识之间的平衡因素；我们的方法称为梯度情节内存，具有软约束$ε$（$ε$ -soft-gem）。 $ε$ -SOFT-GEM在单个培训时期的表现优于A-GEM和几个持续学习基准；此外，它具有最先进的计算和记忆（例如A-GEM）的平均准确性和效率，并在保持学习知识的稳定性与学习新知识的可塑性之间提供了更好的权衡。
Catastrophic forgetting in continual learning is a common destructive phenomenon in gradient-based neural networks that learn sequential tasks, and it is much different from forgetting in humans, who can learn and accumulate knowledge throughout their whole lives. Catastrophic forgetting is the fatal shortcoming of a large decrease in performance on previous tasks when the model is learning a novel task. To alleviate this problem, the model should have the capacity to learn new knowledge and preserve learned knowledge. We propose an average gradient episodic memory (A-GEM) with a soft constraint $ε\in [0, 1]$, which is a balance factor between learning new knowledge and preserving learned knowledge; our method is called gradient episodic memory with a soft constraint $ε$ ($ε$-SOFT-GEM). $ε$-SOFT-GEM outperforms A-GEM and several continual learning benchmarks in a single training epoch; additionally, it has state-of-the-art average accuracy and efficiency for computation and memory, like A-GEM, and provides a better trade-off between the stability of preserving learned knowledge and the plasticity of learning new knowledge.
