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Related Questions
- What are the key differences between in-domain and out-of-domain generalization in the context of pre-trained models?
- Can you provide examples of in-domain generalization in pre-trained models, such as language models or image classification models?
- How do pre-trained models handle out-of-domain data, and what are the potential consequences of poor out-of-domain generalization?
- What techniques can be used to improve the generalizability of pre-trained models to out-of-domain data?
- Can you explain the concept of 'domain shift' and its relationship to in-domain and out-of-domain generalization?
- How do pre-trained models handle the concept of 'category shift' versus 'domain shift', and what are the implications for model performance?
- What are some common pitfalls to avoid when evaluating the out-of-domain generalization of pre-trained models?
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