Pseudo group-guided and reliability-aware debiasing framework for unsupervised bias mitigation

Deep neural networks often exploit spurious correlations between target labels and task-irrelevant attributes, leading to substantial performance degradation on samples that deviate from dominant spurious patterns. To address this issue, we propose a selective and reliability-aware debiasing framework for settings where spurious attributes are unavailable. Our approach extends representation-based pseudo-group discovery through Gaussian Noise Intervention (GNI), which evaluates the stability of intra-class geometric evidence under bounded local perturbations and derives a conflict score for selective pseudo-group inference. Within each target class, samples are divided into pseudo-aligned, pseudo-conflicting, and uncertain groups. Retained pseudo-conflicting samples are further assigned reliability scores based on multi-view consistency and class-wise ranking. These pseudo-group assignments and reliability estimates are incorporated into a reliability-weighted multi-level debiasing objective, where they play distinct roles in emphasizing bias-conflicting samples and modulating their debiasing contributions. To further mitigate residual shortcut influence, we employ trained bias experts to perform Debiased Correction Of Residual Effects (D-CORE) at inference stage. Experiments on medical and natural-image benchmarks demonstrate improved worst-group robustness across most evaluated settings while maintaining competitive average accuracy.