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NVIDIA Generative AI Multimodal Sample Questions:

1. You are developing a text-to-image generative model and want to evaluate the quality and diversity of the generated images. Which metric is MOST appropriate for assessing the diversity of generated images, considering computational efficiency is also important?

A) Inception Score (IS)
B) Multi-Scale Structural Similarity Index (MS-SSIM)
C) Learned Perceptual Image Patch Similarity (LPIPS)
D) Frechet Inception Distance (FID)
E) Number of unique images in the generated set.

2. You are building an image generation pipeline that leverages both a U-Net and a pre-trained CLIP model. After generating an image with the U-Net, you want to use CLIP to assess how well the generated image aligns with a given text prompt. Which of the following steps are crucial for obtaining a meaningful similarity score between the image and the text using CLIP?

A) Encode the generated image using CLIP's image encoder.
B) Encode the text prompt using CLIP's text encoder.
C) Fine-tune the CLIP model on your specific image generation task.
D) Calculate the cosine similarity between the image and text embeddings.
E) Resize the generated image to a very high resolution.

3. Which of the following regularization techniques is MOST effective for preventing overfitting in a multimodal deep learning model with a large number of parameters and complex interactions between different modalities?

A) Early Stopping
B) L2 regularization
C) Dropout
D) Batch Normalization
E) L1 regularization

4. You are designing a IJ-Net architecture for semantic segmentation of medical images. Your input images are 512x512 with 3 channels.
You want to ensure the final output segmentation map is also 512x512. Which of the following design choices are crucial for achieving this resolution, considering the downsampling and upsampling stages?

A) Using max pooling with a kernel size of 3x3 and stride of 2 for downsampling, and nearest neighbor interpolation for upsampling.
B) Employing only IXI convolutions in the bottleneck of the U-Net architecture to reduce computational complexity.
C) Ensuring that the number of downsampling and upsampling blocks are equal, and employing skip connections from corresponding encoder layers to decoder layers.
D) Using a batch size of 1 during training to simplify memory management.
E) Using only strided convolutions for downsampling and transposed convolutions for upsampling without skip connections.

5. Consider a system that generates captions for images, and a key metric is BLEU score. You observe that while the BLEU score is high, the generated captions often lack detailed descriptions of the objects and relationships within the image. Which of the following strategies would you employ to improve the descriptive richness of the generated captions?

A) Increase the beam size during decoding to explore a wider range of possible captions.
B) Train the model to minimize cross-entropy loss between predicted and ground truth captions.
C) Implement early stopping based solely on BLEU score during training.
D) Fine-tune the model using Reinforcement Learning with a reward function that encourages detailed descriptions, such as CIDEr or SPICE.
E) Reduce the size of the vocabulary to focus on the most common words.

Solutions: