Transferabhängigkeiten

Transferabhängigkeiten, also known as transfer dependencies, refer to the situation where the success or performance of a machine learning model in a new task (the target task) is influenced by the knowledge or weights learned from a previous task (the source task). This is a fundamental concept in transfer learning, a subfield of machine learning that aims to improve learning in a new task by leveraging knowledge from a related task that has already been learned.

The existence of transfer dependencies implies that the source task is not entirely irrelevant to the target task. If there were no transfer dependencies, training a model from scratch on the target task would yield the same or better results. Positive transfer dependencies occur when the knowledge from the source task is beneficial for the target task, leading to faster convergence, better accuracy, or a need for less training data. Conversely, negative transfer dependencies arise when the source task knowledge hinders learning on the target task, resulting in worse performance compared to training from scratch. Identifying and quantifying these dependencies is crucial for effectively applying transfer learning techniques. Researchers often study transfer dependencies to understand how to select appropriate source tasks, pre-trained models, and transfer learning strategies for specific target tasks. Factors influencing transfer dependencies include the similarity between the source and target tasks, the amount of data available for each task, and the chosen model architecture.