IDE: A System for Iterative Mislabel Detection

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.