Rehearsal-based techniques are commonly used to mitigate catastrophic
forgetting (CF) in Incremental learning (IL). The quality of the exemplars
selected is important for this purpose and most methods do not ensure the
appropriate diversity of the selected exemplars. We propose a new technique
"DSS" -- Diverse Selection of Samples from the input data stream in the
Class-incremental learning (CIL) setup under both disjoint and fuzzy task
boundary scenarios. Our method outperforms state-of-the-art methods and is much
simpler to understand and implement.

DSS 是一种新的技术，用于从数据流中选择样本来减轻增量学习中的灾难性遗忘，该方法在分离和模糊任务边界情景下比现有方法表现更好，而且更易理解和实现。

DSS：一种用于保留分类增量学习中知识的多样样本选择方法

DSS: A Diverse Sample Selection Method to Preserve Knowledge in  Class-Incremental Learning

Continual learning aims to improve the ability of modern learning systems to
deal with non-stationary distributions, typically by attempting to learn a
series of tasks sequentially. Prior art in the field has largely considered
supervised or reinforcement learning tasks, and often assumes full knowledge of
task labels and boundaries. In this work, we propose an approach (CURL) to
tackle a more general problem that we will refer to as unsupervised continual
learning. The focus is on learning representations without any knowledge about
task identity, and we explore scenarios when there are abrupt changes between
tasks, smooth transitions from one task to another, or even when the data is
shuffled. The proposed approach performs task inference directly within the
model, is able to dynamically expand to capture new concepts over its lifetime,
and incorporates additional rehearsal-based techniques to deal with
catastrophic forgetting. We demonstrate the efficacy of CURL in an unsupervised
learning setting with MNIST and Omniglot, where the lack of labels ensures no
information is leaked about the task. Further, we demonstrate strong
performance compared to prior art in an i.i.d setting, or when adapting the
technique to supervised tasks such as incremental class learning.

本研究提出了一种名为 CURL 的方法，用于解决无监督的连续学习问题，通过学习动态的任务表示，以应对任务标识的缺乏，探索了任务之间的突然变化、平滑过渡和数据乱序等不同情况，并在 MNIST 和 Omniglot 数据集上证明了其强大性能。