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Label Delay in Online Continual Learning
April 29, 2024, 4:42 a.m. | Botos Csaba, Wenxuan Zhang, Matthias M\"uller, Ser-Nam Lim, Mohamed Elhoseiny, Philip Torr, Adel Bibi
cs.LG updates on arXiv.org arxiv.org
Abstract: Online continual learning, the process of training models on streaming data, has gained increasing attention in recent years. However, a critical aspect often overlooked is the label delay, where new data may not be labeled due to slow and costly annotation processes. We introduce a new continual learning framework with explicit modeling of the label delay between data and label streams over time steps. In each step, the framework reveals both unlabeled data from the …
abstract annotation arxiv attention continual cs.cv cs.lg data delay framework however process processes streaming streaming data training training models type
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