Domänenanpassung

Domänenanpassung, also known as domain adaptation, is a subfield of machine learning that deals with the problem of applying a model trained on a source domain to a target domain where the data distribution differs. The core challenge lies in the fact that a model performing well on the source data may not generalize effectively to the target data due to these distribution shifts.

The goal of domain adaptation is to leverage the knowledge learned from the labeled source domain to improve performance on the unlabeled or sparsely labeled target domain. This is particularly useful when obtaining large amounts of labeled data for the target domain is expensive or impractical.

Various techniques exist to achieve domain adaptation. One common approach involves learning domain-invariant features, which are representations that are similar across both source and target domains. This can be achieved through methods like adversarial training, where a domain discriminator tries to distinguish between source and target features, and the feature extractor is trained to fool this discriminator.

Another strategy is to adapt the model parameters directly, either by fine-tuning the source model on target data or by applying parameter transformations. Instance weighting, where source instances are weighted based on their similarity to the target domain, is another method.

Domain adaptation is crucial in many real-world applications, such as adapting image recognition models trained on one dataset to a different dataset with varying lighting or viewpoints, or applying natural language processing models trained on news articles to social media text.