Representationsöverföring

Representationsöverföring, often translated as representation transfer or knowledge transfer, is a concept in machine learning and artificial intelligence that deals with leveraging knowledge gained from one task or domain to improve performance on a different, but related, task or domain. The core idea is that a model trained on a large dataset for a general task can learn useful underlying representations of data that can be beneficial when applied to a new task with limited data.

This process typically involves using a pre-trained model as a starting point. The pre-trained model, having learned general features from a source task, is then adapted for a target task. This adaptation can take several forms. One common approach is feature extraction, where the pre-trained model's layers are used to generate features for the new task, and a new classifier or regressor is trained on top of these features. Another method is fine-tuning, where the weights of the pre-trained model are further adjusted using data from the target task, allowing the model to specialize while retaining the general knowledge.

Representationsöverföring is particularly valuable when the target task has scarce labeled data, as training a complex model from scratch would likely lead to overfitting. By transferring representations, the model benefits from the extensive learning done on the source task, requiring less data and computational resources for the target task. This technique is widely applied in areas such as computer vision, natural language processing, and speech recognition, accelerating development and improving performance in numerous applications.