Bias Aligned Sample

Bias-aligned samples, data points exhibiting spurious correlations with target labels, are a central challenge in machine learning, hindering model generalization and fairness. Current research focuses on identifying and mitigating the influence of these samples through various techniques, including anomaly detection, sample synthesis (e.g., using diffusion models), and data re-weighting strategies that emphasize bias-conflicting samples (those lacking spurious correlations). These efforts aim to improve model robustness and fairness, leading to more reliable and equitable machine learning systems across diverse applications.