2025
Let Samples Speak: Mitigating Spurious Correlation by Exploiting the Clusterness of Samples
CVPR 2025poster
Deep learning models are known to often learn features that spuriously correlate with the class label during training but are irrelevant to the prediction task. Existing methods typically address this issue by annotating potential spurious attributes, or filtering spurious features based on some emp…