Task Free online continual learning (TF-CL) is a challenging problem where the model incrementally learns tasks without explicit task information. Although training with entire data from the past, present as well as future is considered as the gold standard, naive approaches in TF-CL with the current samples may be conflicted with learning with samples in the future, leading to catastrophic forgetting and poor plasticity. Thus, a proactive consideration of an unseen future sample in TF-CL becomes imperative. Motivated by this intuition, we propose a novel TF-CL framework considering future samples and show that injecting adversarial perturbations on both input data and decision-making is effective. Then, we propose a novel method named Doubly Perturbed Continual Learning (DPCL) to efficiently implement these input and decision-making perturbations. Specifically, for input perturbation, we propose an approximate perturbation method that injects noise into the input data as well as the feature vector and then interpolates the two perturbed samples. For decision-making process perturbation, we devise multiple stochastic classifiers. We also investigate a memory management scheme and learning rate scheduling reflecting our proposed double perturbations. We demonstrate that our proposed method outperforms the state-of-the-art baseline methods by large margins on various TF-CL benchmarks.

翻译：无任务在线持续学习（TF-CL）是一个具有挑战性的问题，要求模型在没有明确任务信息的情况下逐步学习任务。尽管使用过去、现在及未来的全部数据进行训练被认为是黄金标准，但TF-CL中基于当前样本的朴素方法可能与未来样本的学习产生冲突，导致灾难性遗忘和可塑性不足。因此，在TF-CL中主动考虑未见过的未来样本变得至关重要。基于这一直觉，我们提出了一种新颖的TF-CL框架，该框架考虑了未来样本，并表明在输入数据和决策过程中注入对抗性扰动是有效的。随后，我们提出了一种名为双扰动持续学习（DPCL）的新方法，以高效实现这些输入与决策扰动。具体而言，对于输入扰动，我们提出了一种近似扰动方法，向输入数据及特征向量注入噪声，并对两个扰动样本进行插值。对于决策过程扰动，我们设计了多个随机分类器。我们还研究了反映所提出的双重扰动的内存管理方案和学习率调度策略。实验证明，我们的方法在多个TF-CL基准测试中大幅优于最先进的基线方法。