Catastrophic forgetting poses a significant challenge in continual learning,
where models often forget previous tasks when trained on new data. Our
empirical analysis reveals a strong correlation between catastrophic forgetting
and the learning speed of examples: examples learned early are rarely
forgotten, while those learned later are more susceptible to forgetting. We
demonstrate that replay-based continual learning methods can leverage this
phenomenon by focusing on mid-learned examples for rehearsal. We introduce
Goldilocks, a novel replay buffer sampling method that filters out examples
learned too quickly or too slowly, keeping those learned at an intermediate
speed. Goldilocks improves existing continual learning algorithms, leading to
state-of-the-art performance across several image classification tasks.

通过分析发现，catastrophic forgetting 与学习速度存在强烈相关性，我们提出了一种名为 Goldilocks 的回放缓冲采样方法，该方法通过筛选中间速度学习的示例来改进现有的持续学习算法，从而在几个图像分类任务中实现了最先进的性能。

连续学习中的遗忘顺序：先学的例子最后遗忘

Forgetting Order of Continual Learning: Examples That are Learned First  are Forgotten Last

Predictive Autoscaling is used to forecast the workloads of servers and
prepare the resources in advance to ensure service level objectives (SLOs) in
dynamic cloud environments.However, in practice, its prediction task often
suffers from performance degradation under abnormal traffics caused by external
events (such as sales promotional activities and applications'
re-configurations), for which a common solution is to re-train the model with
data of a long historical period, but at the expense of high computational and
storage costs.To better address this problem, we propose a replay-based
continual learning method, i.e., Density-based Memory Selection and Hint-based
Network Learning Model (DMSHM), using only a small part of the historical log
to achieve accurate predictions.First, we discover the phenomenon of sample
overlap when applying replay-based continual learning in prediction tasks. In
order to surmount this challenge and effectively integrate new sample
distribution, we propose a density-based sample selection strategy that
utilizes kernel density estimation to calculate sample density as a reference
to compute sample weight, and employs weight sampling to construct a new memory
set.Then we implement hint-based network learning based on hint representation
to optimize the parameters.Finally, we conduct experiments on public and
industrial datasets to demonstrate that our proposed method outperforms
state-of-the-art continual learning methods in terms of memory capacity and
prediction accuracy. Furthermore, we demonstrate remarkable practicability of
DMSHM in real industrial applications.

在动态云环境中，使用预测自动缩放来预测服务器的工作负载，并提前准备资源以确保服务水平目标（SLOs）。然而，在实践中，由于外部事件（例如销售促销活动和应用程序重新配置）导致的异常流量，其预测任务通常会遭受性能下降的影响。针对这个问题，我们提出了一种基于回放的持续学习方法，即基于密度的内存选择和基于提示的网络学习模型（DMSHM），只使用历史记录的一小部分来实现准确的预测。我们同时还展示了 DMSHM 在真实工业应用中杰出的实用性。

预测性自动缩放中的持续学习

Continual Learning in Predictive Autoscaling

A major problem with Active Learning (AL) is high training costs since models
are typically retrained from scratch after every query round. We start by
demonstrating that standard AL on neural networks with warm starting fails,
both to accelerate training and to avoid catastrophic forgetting when using
fine-tuning over AL query rounds. We then develop a new class of techniques,
circumventing this problem, by biasing further training towards previously
labeled sets. We accomplish this by employing existing, and developing novel,
replay-based Continual Learning (CL) algorithms that are effective at quickly
learning the new without forgetting the old, especially when data comes from an
evolving distribution. We call this paradigm Continual Active Learning (CAL).
We show CAL achieves significant speedups using a plethora of replay schemes
that use model distillation and that select diverse, uncertain points from the
history. We conduct experiments across many data domains, including natural
language, vision, medical imaging, and computational biology, each with
different neural architectures and dataset sizes. CAL consistently provides a
3x reduction in training time, while retaining performance.

本研究提出 Continual Active Learning（CAL）的概念，结合现有和新开发的基于回放的多样化技术，能有效解决 Active Learning 中重新训练以及忘记旧数据等问题，从而能在保持性能的同时大幅度减小训练时间。