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.

通过考虑未来样本和注入敌对扰动，我们提出了一种新的 TF-CL 框架，并通过在输入数据和决策过程中注入敌对扰动的方法实现了这一框架，通过采用记忆管理方案和学习率调度进一步提高性能。