Domain adaptation strategies are techniques used in machine learning to enable a model trained on one domain to perform well on a different but related domain. These strategies are crucial for improving model generalization and reducing the need for extensive retraining when faced with varying conditions or environments. By leveraging knowledge from the source domain, these strategies help to bridge the gap between training and application scenarios, enhancing performance in real-world tasks.
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