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NVIDIA Generative AI Multimodal Sample Questions (Q52-Q57):
NEW QUESTION # 52
You are experimenting with different architectures for a text-to-speech (TTS) model. You have implemented a Tacotron 2 model and a FastSpeech 2 model. Which of the following statements accurately describes the key differences between these two architectures and their implications?
Answer: C,D
Explanation:
Tacotron 2 is an autoregressive model that uses an attention mechanism for aligning text and speech, whereas FastSpeech 2 is a non-autoregressive model, generating speech in parallel for faster inference. FastSpeech 2 also addresses the one-to-many mapping problem (one phoneme can have different durations) with length regulator and variance adaptor modules, improving stability and controllability. A is incorrect because attention based Tacotron 2 is slow to train and infer. B and D are incorrect becuase Tacotron 2 architecture has attention mechanism.
NEW QUESTION # 53
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?
Answer: A
Explanation:
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.
NEW QUESTION # 54
Consider a scenario where you are developing a multimodal system for generating 3D models from text descriptions. The system uses a Variational Autoencoder (VAE) to generate the 3D models. During training, you observe that the generated 3D models lack diversity and tend to cluster around a few common shapes. Which of the following techniques could you employ to improve the diversity of the generated 3D models?
Answer: A,C
Explanation:
A larger, more diverse dataset provides more examples for the VAE to learn from, leading to more diverse generated outputs. Adversarial training can help the VAE generate more realistic and diverse 3D models by penalizing outputs that are easily distinguishable from real data. Decreasing the latent space capacity can limit the model's ability to capture the diversity of the data. Increasing the KL divergence weight can lead to underfitting and less diverse outputs. Decreasing batch size can increase the variance of gradients during training, but its impact on diversity is less direct.
NEW QUESTION # 55
You're tasked with building a model that can generate recipes from images of food. You decide to use a Variational Autoencoder (VAE) architecture. What would be a suitable loss function combination for this task, considering both reconstruction accuracy and recipe relevance?
Answer: B
Explanation:
The Reconstruction loss ensures the generated image is similar to the input. KL divergence enforces a smooth latent space. The Cross-entropy loss ensures the generated recipe is relevant to the decoded image. Perceptual loss, while helpful for image quality, doesn't directly address recipe relevance. Using a text embedding of a random recipe would not guide the model towards generating relevant recipes.
NEW QUESTION # 56
You are building a multimodal model for medical image diagnosis, using both radiology images (e.g., X-rays) and patient clinical notes.
The clinical notes are highly unstructured and contain significant medical jargon. What preprocessing steps would be MOST effective for improving the model's performance?
Answer: B
Explanation:
For medical text, NER and BioBERT are the best choice. NER extracts relevant medical information, while BioBERT is pre-trained on a large corpus of biomedical text, allowing it to better understand medical jargon and context. Raw text (A) would be ineffective. Basic cleaning and Word2Vec (B) are insufficient for complex medical language. Translation (D) isn't the primary preprocessing step needed. Sentiment analysis (E) is You have a multimodal generative model that produces images from text descriptions.
NEW QUESTION # 57
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