How can ML and AI technologies be leveraged to automatically annotate data, thereby reducing the need for manual labeling?
How does semi-supervised learning help decrease the demand for labeled data while enhancing model performance?
What role can pre-trained models and transfer learning play in accelerating ground truth data generation?
How can active learning methods be employed to select the most informative samples for annotation, thus minimizing labeling efforts?
What techniques can be used to synthesize data and apply data augmentation, effectively expanding the ground truth dataset and improving model generalization?