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Found 4 papers, 3 papers with code
Learning Robust Classifiers with Self-Guided Spurious Correlation Mitigation
The fine-grained training labels are formulated with different prediction behaviors of the classifier identified in a novel spuriousness embedding space.
Spurious Correlations in Machine Learning: A Survey
Machine learning systems are known to be sensitive to spurious correlations between biased features of the inputs (e. g., background, texture, and secondary objects) and the corresponding labels.
AdvST: Revisiting Data Augmentations for Single Domain Generalization
Then, we propose Adversarial learning with Semantics Transformations (AdvST) that augments the source domain data with semantics transformations and learns a robust model with the augmented data.
Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection
Experiments show the effectiveness of the proposed method in providing explanations that are both faithful to models and interpretable to humans.
