Semantic Imbalance

Semantic imbalance in machine learning refers to the uneven distribution of meaningful information across classes or concepts within datasets, leading to biased model predictions and poor generalization. Current research focuses on developing methods to diagnose and mitigate this imbalance, employing techniques like concept graph representations, data augmentation strategies tailored to specific imbalances (e.g., label, scale, or concept-level), and novel loss functions that re-weight samples or concepts based on their semantic diversity. Addressing semantic imbalance is crucial for improving the reliability and fairness of machine learning models across various applications, including image recognition, object detection, and scene graph generation, ultimately leading to more robust and accurate AI systems.