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cs.LG2022
Learning to Detect Noisy Labels Using Model-Based Features
Zhihao Wang, Zongyu Lin, Peiqi Liu +5
Label noise is ubiquitous in various machine learning scenarios such as self-labeling with model predictions and erroneous data annotation. Many existing approaches are based on he…
cs.LG2022
TCNL: Transparent and Controllable Network Learning Via Embedding Human-Guided Concepts
Zhihao Wang, Chuang Zhu
Explaining deep learning models is of vital importance for understanding artificial intelligence systems, improving safety, and evaluating fairness. To better understand and contro…