Unsupervised corrupt data detection for text training

Peiyang Liu

Abstract: Highlights•Real-world datasets’ label noise impacts model learning.•Conventional methods focus on visual data.•Supervised approaches risk overfitting.•New unsupervised technique detects corrupt text.•Study advances detection and text-based models.