  In the realm of continual learning, the presence of noisy labels within data
streams represents a notable obstacle to model reliability and fairness. We
focus on the data stream scenario outlined in pertinent literature,
characterized by fuzzy task boundaries and noisy labels. To address this
challenge, we introduce a novel and intuitive sampling method called Noisy Test
Debiasing (NTD) to mitigate noisy labels in evolving data streams and establish
a fair and robust continual learning algorithm. NTD is straightforward to
implement, making it feasible across various scenarios. Our experiments
benchmark four datasets, including two synthetic noise datasets (CIFAR10 and
CIFAR100) and real-world noise datasets (mini-WebVision and Food-101N). The
results validate the efficacy of NTD for online continual learning in scenarios
with noisy labels in data streams. Compared to the previous leading approach,
NTD achieves a training speedup enhancement over two times while maintaining or
surpassing accuracy levels. Moreover, NTD utilizes less than one-fifth of the
GPU memory resources compared to previous leading methods.
