Crowdsourced Label

Crowdsourced labeling leverages human annotators to label large datasets for machine learning, addressing the cost and time constraints of expert annotation. Current research focuses on improving label quality by mitigating annotator disagreement and bias through techniques like incorporating annotator confidence, context-aware labeling strategies, and novel data acquisition methods such as patch labeling. These advancements aim to create more reliable and efficient training datasets, ultimately improving the accuracy and robustness of machine learning models across various applications, from natural language processing to image classification.