Example Mining

Example mining is a technique used to improve the performance of machine learning models by strategically selecting and weighting training examples. Current research focuses on adapting this technique for various tasks, including data visualization, 3D object detection, and voice spoofing detection, often employing large language models or specialized loss functions to identify and prioritize "hard" or "rare" examples that are challenging for the model to learn. This approach addresses the limitations of standard training methods, particularly in scenarios with imbalanced datasets or significant intra-class variation, leading to more robust and accurate models across diverse applications. The impact is seen in improved accuracy and generalization capabilities, particularly for under-represented data points, ultimately advancing the reliability and effectiveness of machine learning systems.