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Related Questions
- What are some common challenges in adapting a machine learning model to a new domain, and how can they be addressed?
- Can you explain the difference between unsupervised and supervised domain adaptation, and which one is more suitable for complex datasets?
- How does domain adaptation technique affect the performance of a model when there is a large class imbalance in the target domain?
- What are some popular domain adaptation techniques used in computer vision, and how do they improve model performance?
- In what scenarios is multi-task learning a suitable domain adaptation technique, and how does it benefit the model's performance?
- How can self-paced learning be used to adapt a model to a new domain, especially when there is a lack of labeled data in the target domain?
- What are some common evaluation metrics used to assess the performance of a model after domain adaptation, and how do they help in comparing different adaptation techniques?
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