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NVIDIA Generative AI Multimodal Sample Questions:

1. You're training a generative adversarial network (GAN) for multimodal image synthesis. The GAN takes text descriptions as input and generates corresponding images. You observe that the generator consistently produces images that are semantically related to the text but lack fine-grained details.
Which of the following loss functions, when combined with the standard GAN loss, would be MOST effective in improving the image quality and realism?

A) Mean Squared Error (MSE) loss between the generated image and a real image from the training set.
B) Cosine Similarity loss between generated image and a real image.
C) Perceptual loss based on features extracted from a pre-trained convolutional neural network (CNN).
D) L1 loss between the generated image and the text embedding.
E) Cross-entropy loss between the generated image and the text description.

2. Which of the following are valid methods for addressing the vanishing gradient problem in deep neural networks?

A) Using ReLU (Rectified Linear Unit) activation functions.
B) Using batch normalization.
C) Increasing the learning rate.
D) Employing skip connections (e.g., in ResNets).
E) Using sigmoid activation functions.

3. Consider the following code snippet using a hypothetical Generative A1 library. This code is intended to generate an image from a text prompt and then refine it based on a user-provided style image. However, it's not producing the desired results. What is the MOST likely cause of the issue?

A) The 'generate_image' function does not support the parameter.
B) The library being used is incompatible with the GPU.
C) The text prompt provided is too short.
D) The 'strength' parameter in 'refine_image' is set too low, resulting in minimal stylistic changes.
E) The 'style_image' is not preprocessed correctly before being passed to the 'refine_image' function.

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

5. You have a multimodal model combining video and text data for action recognition. The model performs well on standard datasets but struggles with videos containing unusual camera angles or lighting conditions. Which data augmentation strategy would be MOST effective in improving the model's robustness?

A) Replacing random words in the text descriptions with synonyms.
B) Randomly cropping and scaling the video frames.
C) Adding random noise to the audio track.
D) Applying random rotations, flips, and color jittering to the video frames.
E) Reducing the frame rate of the videos.

Solutions: