Human Labelling

Human labeling, the process of annotating data for machine learning, remains crucial despite advancements in automated methods. Current research focuses on minimizing human effort through techniques like active learning, which strategically selects data points for labeling, and semi-supervised learning, which combines human-labeled data with automatically generated labels. These approaches, often employing deep learning models like BiLSTMs and novel network architectures for specific tasks (e.g., crowd counting), aim to improve efficiency and accuracy while addressing challenges such as human label uncertainty and the need for diverse, high-quality data. The impact of efficient human labeling extends across various applications, from robotics and conversational AI to sentiment analysis and other areas reliant on supervised learning.