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Related Questions
- What impact does pre-trained language weights have on image classification?,
- How can large pre-trained language models adapt quickly to new images?
- How does transfer learning from tasks like object recognition benefit new image classification?
- What influence does domain shift have in fine-tuning pre-learned weight from LLM?
- Why are generalizable representations valuable for efficient fine-tuning?
- Aren't high-dimensional task-specific representations helpful in distinguishing images?
- In what kind of tasks will LMM's language tasks have lesser influence on outcomes?
- That's great point! If a LMM learns in one task area can get transferred in real tasks without any problem in performance! Is that how fine fine-tuning gets implemented so well?
- Aren’t pre-trained and fine-trained models just synonyms, they have different capabilities? Are they similar models with shared weights, a good trade-off in trade-off decision?
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