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Related Questions
- Can catastrophic forgetting occur when fine-tuning a pre-trained model on a new task, and if so, how can it be mitigated?
- How does the degree of catastrophic forgetting vary with the size and complexity of the new task compared to the original one?
- Do different fine-tuning strategies, such as few-shot learning or incremental learning, affect the likelihood of catastrophic forgetting in pre-trained models?
- Can the use of techniques like knowledge distillation or multi-task learning help to reduce catastrophic forgetting in fine-tuned models?
- What is the relationship between model capacity and the likelihood of catastrophic forgetting in fine-tuned pre-trained models?
- Can the type of training data used for fine-tuning, such as supervised or semi-supervised learning, impact the occurrence of catastrophic forgetting?
- Are there any specific architectures or design choices that can help mitigate catastrophic forgetting in fine-tuned pre-trained models?
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