The Re-Label Method For Data-Centric Machine Learning

• URL: http://arxiv.org/abs/2302.04391v9
• Date: Fri, 22 Nov 2024 01:41:55 GMT
• Title: The Re-Label Method For Data-Centric Machine Learning
• Authors: Tong Guo,
• Abstract summary: In industry deep learning application, our manually labeled data has a certain number of noisy data. 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.
• Score: 0.24475591916185496
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 prediction. The dev dataset evaluation results and human evaluation results verify our idea.

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