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Related Questions
- What are the common challenges faced by deep neural networks when generalizing to out-of-distribution data?
- How does in-distribution generalization differ from out-of-distribution generalization in the context of machine learning?
- Can you provide examples of scenarios where in-distribution generalization performs well, but out-of-distribution generalization fails?
- What role does data augmentation play in improving out-of-distribution generalization?
- How can we measure and evaluate the performance of in-distribution and out-of-distribution generalization in a model?
- What are some techniques to improve out-of-distribution generalization, such as adversarial training or learning to learn?
- Can you explain the difference between in-distribution generalization and robustness, and how they relate to each other?
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