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Found 3 papers, 2 papers with code
DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations
Deep learning classifiers are prone to latching onto dominant confounders present in a dataset rather than on the causal markers associated with the target class, leading to poor generalization and biased predictions.
Debiasing Counterfactuals In the Presence of Spurious Correlations
Deep learning models can perform well in complex medical imaging classification tasks, even when basing their conclusions on spurious correlations (i. e. confounders), should they be prevalent in the training dataset, rather than on the causal image markers of interest.
SwinCheX: Multi-label classification on chest X-ray images with transformers
Our model has been tested with several number of MLP layers for the head setting, each achieves a competitive AUC score on all classes.
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