DAR replaces GAP with an attention-based aggregation module retrained jointly with the classifier head to disentangle core from spurious features and outperforms DFR on multiple datasets.
In: International Conference on Learning Representations (2020), https://openreview.net/forum?id=ryxGuJrFvS
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
A sample-difficulty decorrelation method that attenuates age-dependent confounding in radiology classification by modeling label-conditioned difficulty trends and applying robust Huber-weighted affinity penalties scaled by an Age Coverage Score.
A worst-group equalized odds regularizer targets extreme subgroup deviations in true and false positive rates to improve multi-attribute fairness in medical imaging while preserving AUC.
citing papers explorer
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Deep Attention Reweighting: Post-Hoc Attention-Based Feature Aggregation in CNNs for Disentangling Core and Spurious Features under Spurious Correlations
DAR replaces GAP with an attention-based aggregation module retrained jointly with the classifier head to disentangle core from spurious features and outperforms DFR on multiple datasets.
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Robust Mitigation of Age-Dependent Confounding Effects via Sample-Difficulty Decorrelation
A sample-difficulty decorrelation method that attenuates age-dependent confounding in radiology classification by modeling label-conditioned difficulty trends and applying robust Huber-weighted affinity penalties scaled by an Age Coverage Score.
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Worst-Group Equalized Odds Regularization for Multi-Attribute Fair Medical Image Classification
A worst-group equalized odds regularizer targets extreme subgroup deviations in true and false positive rates to improve multi-attribute fairness in medical imaging while preserving AUC.