Hard Sample Mining

Hard sample mining (HSM) is a machine learning technique focused on improving model accuracy by prioritizing the training of difficult-to-classify examples. Current research applies HSM across diverse domains, including autonomous navigation, 3D reconstruction (using Neural Radiance Fields), plant disease diagnosis, and fault detection in wind turbines, often in conjunction with deep learning architectures like convolutional neural networks and graph neural networks. By strategically selecting and re-weighting challenging samples, HSM enhances model robustness and efficiency, reducing the need for extensive labeled datasets and improving performance in complex, real-world scenarios. This technique holds significant promise for advancing various applications by improving the accuracy and efficiency of machine learning models.