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Related Questions
- What are some common challenges that models face when dealing with out-of-distribution data?
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- What are some techniques used to handle data that falls outside a model's area of expertise?
- Can a model's performance degrade due to overfitting when it encounters new data?
- What is the difference between out-of-distribution and out-of-sample data, and how do they affect a model's performance?
- How can a model be fine-tuned to handle data that falls outside its original area of expertise?
- What are some real-world examples of models failing to generalize to new data, and what can be learned from these examples?
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