Continual learning is a realistic learning scenario for AI models. Prevalent scenario of continual learning, however, assumes disjoint sets of classes as tasks and is less realistic rather artificial. Instead, we focus on 'blurry' task boundary; where tasks shares classes and is more realistic and practical. To address such task, we argue the importance of diversity of samples in an episodic memory. To enhance the sample diversity in the memory, we propose a novel memory management strategy based on per-sample classification uncertainty and data augmentation, named Rainbow Memory (RM). With extensive empirical validations on MNIST, CIFAR10, CIFAR100, and ImageNet datasets, we show that the proposed method significantly improves the accuracy in blurry continual learning setups, outperforming state of the arts by large margins despite its simplicity. Code and data splits will be available in https://github.com/clovaai/rainbow-memory.

我们提出了一种新的记忆管理策略Rainbow Memory（RM），该策略基于逐样本分类的不确定性和数据增强，旨在增强记忆中的样本多样性，并在MNIST，CIFAR10，CIFAR100和ImageNet数据集上进行了广泛的实证验证，证明了该方法在模糊的继续学习设置中显著提高了准确性，虽然方法简单但超越了目前技术水平。

彩虹记忆：带有多样样本记忆的持续学习