Fine-Tuning vs Feature Extraction

Hard

neural-networks OpenAI Google Anthropic

Multiple Choice

When the target dataset is very small and similar to the pre-training domain, since fine-tuning would risk severe overfitting with so few examples When the target dataset is very large and from a completely different domain, since pre-trained features would be irrelevant When the pre-trained model uses a different architecture than what is needed for the target task When real-time inference speed is critical, since frozen layers always execute faster than trainable ones

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