Label Distribution Shift

Label distribution shift (LDS) describes the problem where the proportions of different classes in a machine learning model's training data differ significantly from those in its testing data. Current research focuses on developing methods to adapt models to these shifts, including techniques like test-time adaptation (TTA) that recalibrate models during inference and algorithms that estimate the target label distribution to improve predictions. Addressing LDS is crucial for building robust and reliable machine learning systems, particularly in applications like medical diagnosis where class prevalence varies across populations or time, improving the generalizability and trustworthiness of AI models in real-world settings.