Domain Adaptation

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Domain adaptation is used when a model is trained in one environment and then deployed somewhere that looks different, even though the task hasn’t changed. For example, the language system may see a different writing style. In these situations, performance can drop in ways that are easy to miss, because standard test results may still look good on the original training setting.

The goal is to reduce that mismatch using whatever information is available from the target environment. Often the target data has few or no labels, so the system has to adjust without clear ground truth. Methods aim to make the training setup better match the target conditions or to learn representations that stay stable across environments. The most important step is to validate on real target data, since the only score that matters is how the model performs where it will actually be used.

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