Domain-Anpassung
Eine Technik des maschinellen Lernens, bei der ein in einer Domäne trainiertes Modell so angepasst wird, dass es in einer anderen, aber verwandten Domäne funktioniert.
Definition
Addresses distribution shifts between source (training) and target (deployment) domains through methods like feature-alignment, adversarial domain classifiers, or fine-tuning on small target-domain labeled samples. Proper governance includes benchmarking adapted models on held-out target data and ensuring no degradation in critical subgroups.
Real-World Example
A speech-recognition model trained on U.S. English accents is adapted for U.K. English using only 10 hours of British-accent recordings. Engineers apply adversarial domain adaptation to align feature spaces, improving word-error rate by 30% on U.K. test sets without retraining from scratch.
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