Data Stream Sampling with Fuzzy Task Boundaries and Noisy Labels

arXiv:2404.04871v1 Announce Type: new
Abstract: 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 …

abstract arxiv continual cs.ai cs.cv cs.lg data data stream data streams fairness focus labels literature realm reliability sampling type