Continual learning is a branch of deep learning that seeks to strike a balance between learning stability and plasticity. In this paper, we specifically focus on the task-free setting where data are streamed online without task metadata and clear task boundaries. A simple and highly effective algorithm class for this setting is known as Experience Replay (ER) that selectively stores data samples from previous experience and leverages them to interleave memory-based and online batch learning updates. Recent advances in ER have proposed novel methods for scoring which samples to store in memory and which memory samples to interleave with online data during learning updates. In this paper, we contribute a novel Adversarial Shapley value ER (ASER) method that scores memory data samples according to their ability to preserve latent decision boundaries for previously observed classes (to maintain learning stability and avoid forgetting) while interfering with latent decision boundaries of current classes being learned (to encourage plasticity and optimal learning of new class boundaries). Overall, we observe that ASER provides competitive or improved performance on a variety of datasets compared to state-of-the-art ER-based continual learning methods.

本文为实现在在线数据流中不断学习新类别而进行的在线类增量设置而贡献了一种新的 Adversarial Shapley 值评分方法，该方法可以为记忆数据样本评分，以便维护先前观察类别的潜在决策边界（以保持学习稳定性和避免遗忘），同时干扰当前正在学习的新类别的潜在决策边界（以鼓励可塑性和最优学习新的类别边界）从而在各种数据集上展现出比现有回放基础的连续学习方法具有竞争性或改进的性能。

基于对抗 Shapley 值的在线课堂增量式连续学习