Random Label

Random label techniques encompass methods where data labels are either randomly assigned, partially revealed, or otherwise manipulated, primarily to address challenges in machine learning such as data scarcity, noisy labels, privacy concerns, and efficient unlearning. Current research focuses on developing robust algorithms and models, including those based on label propagation, conformal prediction, and meta-learning, that can effectively learn from or mitigate the effects of random labels. These techniques are significant for improving the efficiency and reliability of machine learning models, particularly in scenarios with limited or unreliable annotations, and have implications for various applications including data privacy and semi-supervised learning.