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NVIDIA Generative AI Multimodal Sample Questions (Q398-Q403):
NEW QUESTION # 398
You are building a system that generates image captions from images and vice vers a. Which evaluation metric(s) are MOST appropriate to assess the quality of the generated content? (Select all that apply)
Answer: A,C,E
Explanation:
BLEU and ROUGE are common metrics for evaluating text generation quality by comparing generated text to reference text. FID is appropriate for assessing the quality and diversity of generated images. Inception score is also relevant to asses quality and diversity but less so than FID. Accuracy isn't suitable in this context. It's more fitted to classification tasks.
NEW QUESTION # 399
You are working on a generative A1 model that creates descriptions of images. During experimentation, you notice the model consistently generates descriptions that are factually incorrect about objects in the image, despite the image quality being high. For example, it might describe a 'cat' as a 'dog'. What is the MOST critical step to address this issue?
Answer: A
Explanation:
Factually incorrect descriptions indicate a lack of grounding in real-world knowledge. Verifying against an external knowledge base (B) directly addresses this issue. Increasing data size (A) might help, but it's not guaranteed. Fine-tuning (C) and increasing model complexity (D) might not solve the grounding problem. Image sharpening (E) is irrelevant to factual accuracy.
NEW QUESTION # 400
Which of the following are key benefits of using multimodal learning compared to unimodal learning? (Select TWO correct answers)
Answer: A,B
Explanation:
Multimodal learning leverages information from multiple modalities, which can lead to improved robustness because the model can rely on other modalities when one is noisy or incomplete. It also allows the model to learn more complex relationships that might not be apparent from a single modality.
NEW QUESTION # 401
You're building a generative A1 model that can create realistic 3D models from text descriptions. You have a dataset of text descriptions and corresponding 3D models, but the alignment between the text and the 3D models is weak. The model sometimes generates 3D shapes that don't accurately reflect the text. Which of the following techniques could improve the alignment between the text descriptions and the generated 3D models?
Answer: C,D
Explanation:
A contrastive loss function directly encourages the model to learn a mapping between text and 3D models that preserves semantic similarity. Using a pre-trained text encoder allows the model to leverage existing knowledge about language and extract more meaningful features from the text descriptions, improving alignment. Increasing the number of vertices and faces can improve the resolution of the models but won't directly address alignment. 3D data augmentation can improve robustness, but it's less direct. Batch size has a smaller impact compared to the other options.
NEW QUESTION # 402
Consider you are working on a project that aims at generating photorealistic images from segmentation maps, using a conditional GAN architecture. The training process is unstable, frequently exhibiting mode collapse and artifacts. Describe a series of techniques, ranked by their likely impact, to mitigate these issues.
Answer: A
Explanation:
Spectral Normalization stabilizes training by limiting the Lipschitz constant of the discriminator. PatchGAN discriminator focuses on local image patches, improving detail and reducing artifacts. Data augmentation increases the diversity of training data and improves generalization. Thus, Option B presents the most impactful techniques, ranked appropriately. Other options either suggest less impactful techniques or recommend steps that could worsen the issues. Mode Collapse can be avoided here with data augmentation.
NEW QUESTION # 403
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