Instance Sampling

Instance sampling, a crucial aspect of many machine learning tasks, focuses on strategically selecting subsets of data points (instances) to improve model training and performance. Current research emphasizes developing adaptive sampling strategies, such as those driven by reinforcement learning or incorporating instance-level control within generative models, to address issues like class imbalance, computational efficiency, and the need for more robust feature representation. These advancements are significantly impacting various fields, including medical image analysis, object detection in images and videos, and 3D point cloud processing, by enhancing model accuracy, speed, and generalizability.