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Related Questions
- What are the common challenges faced by pre-trained models when applied to out-of-distribution data?
- How does the concept of 'calibration' relate to the performance of pre-trained models on out-of-distribution data?
- What is the role of data augmentation in improving the performance of pre-trained models on out-of-distribution data?
- Can pre-trained models be adapted to new domains using techniques such as transfer learning or domain adaptation?
- What are the differences between 'covariate shift' and 'concept drift' in the context of out-of-distribution data?
- How can pre-trained models be regularized to prevent overfitting on in-distribution data and improve performance on out-of-distribution data?
- What are some common metrics used to evaluate the performance of pre-trained models on out-of-distribution data?
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