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NEW QUESTION # 125
You are developing a system that generates 3D models from text descriptions. The system currently produces models that are geometrically accurate but lack fine-grained surface details and realistic textures. Which of the following steps would be MOST effective in improving the visual realism of the generated 3D models?
Answer: A
Explanation:
Training a separate texture generation model allows for specializing in generating realistic surface details and textures based on both the text description and the underlying 3D geometry. Increasing polygon count (A) can help, but doesn't address texturing. Simplifying the text encoder or reducing the dataset is counterproductive. Solely relying on procedural generation might lead to lack of variability.
NEW QUESTION # 126
You are developing a generative A1 model for medical image segmentation using U-Net architecture. The input images are high- resolution MRI scans. Which of the following techniques would be MOST effective in mitigating the vanishing gradient problem during training, considering memory constraints on your GPU?
Answer: C
Explanation:
Vanishing gradients are a common issue in deep neural networks. Gradient clipping limits the magnitude of gradients, preventing them from becoming too large and destabilizing training. Leaky ReLU and ELU activations help maintain a non-zero gradient even for negative inputs, unlike ReLU. Skip connections are crucial to UNet but do not directly solve the vanishing gradient.
NEW QUESTION # 127
You are building a multimodal generative A1 system that creates 3D models from text descriptions. The system produces accurate shapes but struggles to generate realistic textures and surface details. What approach would BEST address this limitation?
Answer: E
Explanation:
Training a separate texture generation network allows for specialization in generating realistic surface details. This approach decouples the shape generation from the texture generation, allowing each component to be optimized independently. The other options are less targeted at improving texture realism. Increasing text encoder parameters (A) improves text understanding, not texture quality. Reducing resolution (C) degrades the final output. Increasing batch size (D) affects training speed, not texture quality. Adding layers to the shape decoder (E) may improve shape accuracy, but not texture realism.
NEW QUESTION # 128
You are developing a system to automatically generate image descriptions for visually impaired users. The system uses a combination of object detection, attribute recognition, and relationship extraction. However, the generated descriptions often lack detail and fail to capture the nuances of the image content. Which of the following strategies would MOST effectively address this limitation?
Answer: E
Explanation:
Using a powerful transformer-based model enables the system to generate more detailed and nuanced descriptions. Incorporating visual attention allows the model to focus on the most important regions of the image, ensuring that the generated descriptions capture the most relevant aspects of the scene. The other options could help to a degree but do not address the problem holistically.
NEW QUESTION # 129
You are building a multimodal model that combines text and image data to generate captions. The text encoder is a pre-trained BERT model, and the image encoder is a ResNet-50. You observe that the generated captions are heavily biased towards descriptions based on the text input, and the image information is not well represented. Which of the following techniques could you apply to improve the contribution of the image modality?
Answer: A
Explanation:
Applying a modality-specific loss weight allows you to explicitly control the importance of each modality during training. By increasing the weight of the image loss, you encourage the model to pay more attention to the image information and generate captions that are more representative of the visual content. Increasing BERT's learning rate could worsen the imbalance. PCA is a data reduction technique not a balancing technique, freezing the weights on resnet 50, will not allow the network to learn about the relationship, batch size is to do with training speed not modality balance.
NEW QUESTION # 130
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