Transfer Learning

Transfer learning allows knowledge from different machine learning models to be applied to new problems. For this purpose, powerful neural networks from the field of image processing are commonly used as the source models, as this domain provides large datasets for training these models. It is assumed that the source models can create domain-independent features that can also be utilized for other tasks. However, this may require a transformation of the data into a new data representation. This allows the utilization of robust and powerful source models for new tasks, even when only a small amount of data is available, making it impractical to follow a traditional machine learning approach involving training and testing.