The Re-Label Method For Data-Centric Machine Learning

TL;DR

This paper introduces the Re-Label Method, a simple approach that uses model predictions to identify and correct noisy labels in various deep learning tasks, improving data quality and model performance.

Contribution

The paper proposes a novel data cleaning technique that leverages model predictions for re-labeling noisy data across multiple deep learning applications.

Findings

Achieved over 90 score on dev dataset

Verified effectiveness through human evaluation

Applicable to diverse deep learning tasks

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.