Class Distribution Shift

Class distribution shift describes the problem where the frequency of different categories in a dataset changes between training and deployment phases, hindering the performance of machine learning models. Current research focuses on developing robust models that can handle these shifts, employing techniques like synthetic data generation, environment balancing, and curriculum learning to improve generalization across varying class distributions. Addressing this challenge is crucial for reliable deployment of machine learning systems in real-world applications, particularly in areas like autonomous driving and personalized image generation, where data distributions are inherently dynamic and unpredictable.