Label Shift

Label shift, a type of dataset shift, occurs when the distribution of labels changes between training and testing data, while the relationship between features and labels remains consistent. Current research focuses on developing methods to estimate and correct for this shift, employing techniques like importance weighting, kernel methods, and adaptation modules integrated into existing deep learning architectures. Addressing label shift is crucial for improving the reliability and robustness of machine learning models in real-world applications where data distributions inevitably change over time or across domains, impacting model performance and generalization.