IDE: A System for Iterative Mislabel Detection
Yuhao Deng, Qiyan Deng, Chengliang Chai, Lei Cao, Nan Tang, Ju Fan, Jiayi Wang, Ye Yuan, Guoren Wang · 2024
While machine learning techniques, especially deep neural networks, have shown remarkable success in various applications, their performance is adversely affected by label errors in training data. Acquiring high-quality annotated data is both costly and time-consuming in real-world scenarios, requiring extensive human annotation and verification. Consequently, many industry-applied models are trained over data containing substantial noise, significantly degrading the performance of these models.