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NVIDIA Generative AI Multimodal Sample Questions:

1. 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) Increase the batch size during fine-tuning.
C) Use a very high learning rate during fine-tuning.
D) Train the entire model from scratch on the limited paired dataset.
E) Apply L1 regularization to the model weights.

2. Which of the following are potential benefits of using multi-modal learning compared to single-modal learning? (Select all that apply)

A) Reduced risk of overfitting to spurious correlations in a single modality.
B) Increased computational complexity and data requirements.
C) The ability to learn more comprehensive and nuanced representations.
D) Guaranteed higher accuracy across all tasks.
E) Improved robustness to noisy or incomplete data.

3. You are fine-tuning a pre-trained Generative A1 model for a specific downstream task with limited dat a. The model starts to exhibit catastrophic forgetting of the original pre-trained knowledge. Which technique could mitigate this issue effectively?

A) Using a smaller batch size
B) Freezing the weights of earlier layers in the network
C) Elastic Weight Consolidation (EWC)
D) Adding more data augmentation
E) Increasing the learning rate significantly

4. When evaluating a multimodal generative model, which of the following metrics is MOST suitable for assessing the coherence and consistency between the generated image and its corresponding text description?

A) Frechet Inception Distance (FID) for image realism.
B) Perplexity for text generation quality.
C) CLIP score measuring the similarity between image and text embeddings.
D) BLEU score for text fluency.
E) Inception Score (IS) for image quality.

5. You have trained a multimodal model to generate descriptions for recipes, using images of the finished dish as one modality and a list of ingredients as another. When evaluating the generated descriptions, you notice the descriptions are factually correct in terms of ingredients but often fail to capture the stylistic nuances or tone of professionally written recipes. Which evaluation strategy would provide the most insightful feedback on this aspect of the model's performance?

A) Compute the ROUGE score between the generated descriptions and a set of reference recipes.
B) Calculate the BLEU score between the generated descriptions and a set of reference recipes.
C) Measure the perplexity of the generated descriptions using a language model trained on a corpus of recipe books.
D) Conduct a human evaluation where individuals rate the generated descriptions based on factors such as informativeness, clarity, and stylistic appropriateness.
E) Use a pre-trained language model to assess the fluency and coherence of the generated descriptions.

Solutions: