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The Re-Label Method For Data-Centric Machine Learning. (arXiv:2302.04391v6 [cs.LG] UPDATED)
cs.LG updates on arXiv.org arxiv.org
In industry deep learning application, our manually labeled data has a
certain number of noisy data. To solve this problem and achieve more than 90
score in dev dataset, we present a simple method to find the noisy data and
re-label the noisy data by human, given the model predictions as references in
human labeling. In this paper, we illustrate our idea for a broad set of deep
learning tasks, includes classification, sequence tagging, object detection,
sequence generation, click-through rate …
application arxiv data data-centric dataset deep learning dev human industry machine machine learning predictions simple solve