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Found 4 papers, 1 papers with code
Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling
(a) Fairness Intervention (FI): emphasize the minority samples that are hard to generate due to the spurious correlation in the training dataset.
Spread Spurious Attribute: Improving Worst-group Accuracy with Spurious Attribute Estimation
The paradigm of worst-group loss minimization has shown its promise in avoiding to learn spurious correlations, but requires costly additional supervision on spurious attributes.
Learning from Failure: De-biasing Classifier from Biased Classifier
Neural networks often learn to make predictions that overly rely on spurious corre- lation existing in the dataset, which causes the model to be biased.
Learning from Failure: Training Debiased Classifier from Biased Classifier
Neural networks often learn to make predictions that overly rely on spurious correlation existing in the dataset, which causes the model to be biased.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
