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Related Questions
- What are some common techniques for detecting out-of-distribution data in pre-trained models?
- How do pre-trained models handle uncertainty and ambiguity in out-of-distribution data?
- What are some strategies for regularizing pre-trained models to improve their performance on out-of-distribution data?
- How do techniques like data augmentation and adversarial training help mitigate out-of-distribution data issues in pre-trained models?
- What is the role of domain adaptation in handling out-of-distribution data in pre-trained models?
- Can pre-trained models learn to adapt to new domains or tasks with minimal fine-tuning, or is it necessary to start from scratch?
- What are some best practices for selecting and preprocessing out-of-distribution data for use in pre-trained models?
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