To address these challenges, researchers and practitioners are exploring various strategies, such as:
- Data augmentation: Techniques to artificially increase the size and diversity of datasets.
- Transfer learning: Using pre-trained models as a starting point for new tasks or domains.
- Active learning: Selectively collecting and labeling data to optimize the training process.
- Data sharing and collaboration: Encouraging data sharing and collaboration among researchers, organizations, and industries.
- Data annotation and labeling: Developing more efficient and cost-effective methods for annotating and labeling data.
- Data curation and management: Improving data management and curation practices to ensure data quality and accessibility.
- New data sources: Exploring new data sources, such as IoT devices, social media, or citizen science initiatives.