Calibration Data

Calibration data, a small subset of data used to optimize model performance, is crucial for various machine learning and robotics applications, particularly in post-training quantization and pruning of large models where full training datasets are unavailable or impractical. Current research focuses on mitigating overfitting to this limited data, exploring techniques like meta-learning and data augmentation to improve model accuracy and robustness, and investigating the impact of calibration data selection and weighting on downstream task performance. These advancements are vital for deploying efficient and reliable models in resource-constrained environments and improving the accuracy and consistency of sensor calibration in robotics and related fields.