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Related Questions
- Can you explain how self-attention mechanisms in Transformer-based architectures affect generalizability?
- What role does the pre-trained model's capacity for representation learning play in retaining domain-specific knowledge?
- How do early freeze and fine-tuning of pre-trained weights during down-stream adaptation impact overall performance?
- Is there a trade-off between the model's architecture size and its ability to learn domain-specific features from diverse datasets?
- Can you discuss how input sequence length and data-augmentation strategies contribute to the model's adaptability?
- Does the use of an optimizer with a high level of momentum influence the preservation of pre-trained knowledge and avoid catastrophic forgetting?
- How does domain-relevant task-specific weight-tuning influence the models propensity to adapt to target distribution?
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