NCA-GENM Exam Dumps | You're fine-tuning a pre-trained multimodal model for a specific downstream task. You notice that while

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Question 21/192

You're fine-tuning a pre-trained multimodal model for a specific downstream task. You notice that while the model's performance on the training data is excellent, it performs poorly on unseen dat a. What regularization technique, beyond standard weight decay, is MOST likely to improve the model's generalization ability in this scenario, and what is its purpose?

A. Batch Normalization: To accelerate training and reduce internal covariate shift.

B. Dropout: To randomly deactivate neurons during training, preventing co-adaptation and improving robustness.

C. Layer Normalization: To normalize activations across features, stabilizing training.

D. Early Stopping: To halt training when performance on a validation set degrades.

E. Gradient Clipping: To prevent exploding gradients, stabilizing training.

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