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Related Questions
- What are some strategies for fine-tuning pre-trained models to adapt to new tasks and datasets?
- How can researchers ensure that pre-trained models are not perpetuating biases present in the training data?
- What are the trade-offs between using pre-trained models and collecting and labeling diverse and representative data from scratch?
- Can you explain the concept of 'domain adaptation' in the context of transfer learning?
- How can data augmentation techniques be used to increase the diversity and representativeness of pre-trained models?
- What are some best practices for selecting and combining pre-trained models for multi-task learning?
- Can you discuss the role of data curation in balancing the use of pre-trained models with the need for diverse and representative data?
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