DeMix: Debugging Training Data with Mixed Data Error Types by Investigating Influence Vectors

Jiale Deng, Yanyan Shen, Xiaogang Shi +1

High-quality training data is essential for the success of machine learning models. However, real-world datasets often contain mixed types of errors arising from systematic flaws i…