Iterative Sampling

Iterative sampling is a technique used to improve the efficiency and accuracy of various machine learning tasks by strategically selecting subsets of data for training or analysis. Current research focuses on applying iterative sampling to address noisy labels, optimize stochastic algorithms, and enhance self-supervised learning, often leveraging models like CLIP for improved sample selection or employing deep equilibrium frameworks for iterative refinement. These advancements aim to improve model performance, particularly in scenarios with limited data or high label noise, and have implications for diverse applications including image classification, depth estimation, and decentralized optimization.