Sampling Algorithm

Sampling algorithms are crucial for addressing data imbalances and inefficiencies in machine learning, aiming to improve model training and performance. Current research focuses on developing novel oversampling techniques, particularly for imbalanced classification problems, often employing generative models like VAEs or reinforcement learning to synthesize minority class samples effectively and efficiently. These advancements are impacting various fields, from gesture recognition and 3D scene understanding to bot detection and action recognition, by enabling more robust and accurate models even with limited or skewed data. The development of more sophisticated sampling strategies continues to be a significant area of research, driving improvements in model accuracy and efficiency across diverse applications.