Output Imbalance

Output imbalance in machine learning refers to the uneven distribution of classes or data points within a dataset, hindering model training and leading to unfair or inaccurate predictions, particularly for under-represented groups. Current research focuses on developing methods to mitigate this imbalance, including novel loss functions, contrastive learning, and pruning techniques to re-weight samples or focus on "hard-to-learn" instances. Addressing output imbalance is crucial for improving the fairness, robustness, and generalizability of machine learning models across diverse applications, such as anomaly detection, medical image analysis, and defect segmentation in manufacturing.