Domain adaptation techniques are methods used in machine learning and deep learning to adjust a model trained on one domain so that it performs well on another, different but related domain. These techniques help address the challenge of domain shift, which occurs when the training and test data distributions differ significantly, often resulting in poor model performance. By implementing these techniques, models can generalize better to new, unseen data without requiring extensive retraining.
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