Approaches include:
Data Augmentation: Creating additional data samples through techniques like image rotation or text paraphrasing.
Semi-Supervised Learning: Combining a small amount of labeled data with a larger pool of unlabeled data to improve model performance.
Transfer Learning: Using pre-trained models on related tasks and fine-tuning them on the smaller dataset.
Active Learning: Selecting the most informative data points to label, often using model uncertainty to guide the selection process.