Abstract
The data privacy constraint in online continual learning (OCL), where the data can be seen only once, complicates the catastrophic forgetting problem in streaming data. A common approach applied by the current SOTAs in OCL is with the use of memory saving exemplars or features from previous classes to be replayed in the current task. On the other hand, the prompt-based approach performs excellently in continual learning but with the cost of a growing number of trainable parameters. The first approach may not be applicable in practice due to data openness policy, while the second approach has the issue of throughput associated with the streaming data. In this study, we propose a novel prompt-based method for online continual learning that includes 4 main components: (1) single light-weight prompt generator as a general knowledge, (2) trainable scaler-and-shifter as specific knowledge, (3) pre-trained model (PTM) generalization preserving, and (4) hard-soft updates mechanism. Our proposed method achieves significantly higher performance than the current SOTAs in CIFAR100, ImageNet-R, ImageNet-A, and CUB dataset. Our complexity analysis shows that our method requires a relatively smaller number of parameters and achieves moderate training time, inference time, and throughput. For further study, the source code of our method is available at this https URL.
Abstract (translated)
在线连续学习（OCL）中的数据隐私限制，即只能一次性访问的数据，使流数据的灾难性遗忘问题变得更加复杂。当前OCL中领先的解决方案通常采用内存节约示例或从先前类别提取的特征来在当前任务中重放这些信息。另一方面，提示符基础的方法在持续学习中表现出色，但伴随着可训练参数数量不断增加的问题。第一种方法可能由于数据开放政策而在实践中不可行，而第二种方法则面临与流数据相关的吞吐量问题。 在这项研究中，我们提出了一种新颖的基于提示符的方法用于在线连续学习，该方法包括四个主要组成部分：（1）作为通用知识的单个轻量级提示生成器；（2）特定知识可训练缩放和偏移调整器；（3）预训练模型（PTM）泛化保留；以及（4）硬-软更新机制。我们的方法在CIFAR100、ImageNet-R、ImageNet-A 和 CUB 数据集上均显著优于当前最先进的解决方案。复杂性分析表明，与现有方法相比，我们所提出的方法需要更少的参数数量，并且在训练时间、推理时间和吞吐量方面表现出适度水平。 为了进一步研究，我们的代码可以在以下网址获得：[请在此处插入实际链接]。
URL
https://arxiv.org/abs/2507.12305
