2026
Attribution-Guided Model Rectification of Unreliable Neural Network Behaviors
CVPR 2026
The performance of neural network models deteriorates due to their unreliable behavior on non-robust features of corrupted samples. Owing to their opaque nature, rectifying models to address this problem often necessitates arduous data cleaning and model retraining, resulting in huge computational a