Digitization and the steady development in artificial intelligence (AI) research, especially in the field of machine learning (ML), are currently benefiting many companies by enabling them to develop new data-driven business models or reduce process costs (e.g. in manufacturing). An important building block for this AI development is the availability of a large amount of high-quality data.
However, the data is not always available in sufficient quantity, complete, error-free or up-to-date, so that it cannot really be used meaningfully for ML applications. Through methods of data augmentation (DA for short) it is possible to significantly improve data quality and quantity. This allows ML models to be used for the first time in special use cases furthermore the results of existing ML models to be optimized.
Few Data Learning is used in application areas where a very small database is available: for example, in the field of image recognition, especially in medical technology for the diagnosis of tissue anomalies, for computer vision applications in image and video production, or for forecasting and optimization applications in production and logistics.
Existing Few Data Learning methods have been developed for very specific data problems and have different objectives. Therefore, the challenge in research and application is to select, combine and further develop the right Few Data Learning methods for a specific use case.
Within the ADA Lovelace Center, the work of the Few Data Learning competency pillar is closely linked to the Few Labels Learning pillar, which focuses on the annotation of large data sets. This is because in practice, the two problems often occur together: When data is missing, incorrect, or not available in adequate quantity, the corresponding annotations are often missing as well. Therefore, methods from the two competence pillars »Few Data Learning« and »Few labels Learning« are often combined.