Domain Bias

Domain bias, the tendency of machine learning models to favor data from dominant sources, hinders the generalization and robustness of algorithms across diverse datasets. Current research focuses on mitigating this bias through techniques like adversarial training, knowledge distillation, and the development of specialized modules within existing architectures (e.g., Vision Transformers, KNN) to better handle data heterogeneity. Addressing domain bias is crucial for improving the reliability and fairness of machine learning applications across various fields, including crowd counting, fake news detection, and medical image analysis, where data imbalances are common. This leads to more robust and generalizable models that perform well across different domains and avoid perpetuating existing biases in the data.