NCA-GENM Exam Dumps | You're working on a project involving multimodal transfer learning for generating recipes from images

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Question 170/192

You're working on a project involving multimodal transfer learning for generating recipes from images of dishes and ingredient lists. You have a large dataset of images but a limited dataset of paired images and ingredient lists. You decide to leverage a pre-trained image model and a pre-trained text model. However, you are facing catastrophic forgetting after fine-tuning the models on the paired image and ingredient list dat a. Which of the following techniques would be MOST effective in mitigating catastrophic forgetting while adapting the pre-trained models to the new task?

A. Freeze the weights of the pre-trained models and only train a small adapter module that bridges the gap between the pre-trained features and the recipe generation task.

B. Train the entire model from scratch on the limited paired dataset.

C. Use a very high learning rate during fine-tuning.

D. Apply L1 regularization to the model weights.

E. Increase the batch size during fine-tuning.

Correct Answer: A

Using adapter modules is a common technique to mitigate catastrophic forgetting. By freezing most of the pre-trained weights and only training a small adapter, you preserve the knowledge learned during pre-training while adapting the model to the new task. Training from scratch would negate the benefits of transfer learning. A high learning rate can exacerbate forgetting. L1 regularization can prevent overfitting but doesn't directly address forgetting. Increasing batch size might improve generalization but doesn't solve the core issue of catastrophic forgetting.

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