NCA-GENM Exam Dumps | You are tasked with fine-tuning a pre-trained multimodal model for a new task involving image and text

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Question 113/192

You are tasked with fine-tuning a pre-trained multimodal model for a new task involving image and text inputs. The pre-trained model was trained on a large dataset of image-caption pairs. Which of the following strategies would be MOST effective for transfer learning in this scenario, considering computational efficiency and performance?

A. Fine-tune only the classification head (output layer) while freezing all other layers of the pre-trained model.

B. Fine-tune all layers of the pre-trained model with a very small learning rate.

C. Fine-tune a subset of layers, specifically those responsible for feature extraction from both image and text modalities, while keeping the lower layers frozen.

D. Train a new model from scratch on the new task's dataset.

E. Use knowledge distillation to transfer knowledge from the pre-trained model to a smaller, more efficient model.

Correct Answer: C

Option C is the most effective strategy. Fine-tuning a subset of layers allows the model to adapt to the new task while leveraging the pre-trained knowledge. Freezing the lower layers preserves the general features learned from the large dataset, while fine-tuning the feature extraction layers allows the model to learn task-specific features. Fine-tuning all layers (Option B) can lead to overfitting and is computationally expensive. Freezing all layers except the classification head (Option A) may not provide sufficient adaptation. Training from scratch (Option D) is computationally expensive and requires a large dataset. Knowledge distillation (Option E) is also a valid option but may not be the most direct approach for transfer learning when the pre-trained model's architecture is suitable.

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