NCA-GENM Exam Dumps | You are developing a multimodal AI model that processes both text and images to classify news articles

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Question 117/192

You are developing a multimodal AI model that processes both text and images to classify news articles as either 'reliable' or 'unreliable'. After training, you notice that the model performs well on articles with strong visual cues (e.g., professionally edited images), but struggles with articles that have only text or low-quality images. Which of the following techniques would be MOST effective in improving the model's robustness and generalizability across different types of news articles?

A. Exclusively train the model on articles with high-quality images to improve its visual processing capabilities.

B. Implement a modality dropout strategy during training, randomly masking either the text or image input to force the model to rely more on the available modality.

C. Reduce the weight of the image modality in the overall loss function.

D. Replace the image processing component with a simpler, less powerful model.

E. Increase the size of the training dataset by only adding more data with high quality images.

Correct Answer: B

Modality dropout forces the model to learn robust representations from each modality independently, making it less reliant on the presence of both modalities. This improves performance when one modality is missing or of low quality. Training only on high-quality images (A) would exacerbate the problem. Reducing the image modality's weight (C) might help slightly but doesn't fundamentally address the issue. Using a simpler image model (D) would likely decrease overall performance. Increasing the training dataset size with only high-quality images (E) will not address the problem of the model's dependence on high-quality images.

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