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NEW QUESTION # 315
Consider this PyTorch code snippet related to processing multimodal dat a. What is the primary purpose of the following code in the context of Generative A1?
Answer: C
Explanation:
The code defines a custom dataset class ( 'ImageTextDataset' ) which is the standard way in PyTorch to handle datasets that involve paired data, such as images and corresponding text descriptions. This allows for efficient loading and processing of the data during training. The snippet does not directly concatenate, ensure order, or specifically resize the images, though these could be parts of the larger system built upon the dataset class. It also doesn't create separate data loaders, but allows to create one dataset class and loader for the multimodal data.
NEW QUESTION # 316
You have a multimodal model that takes video and audio as input for activity recognition. You want to evaluate the impact of different fusion strategies (early fusion, late fusion, intermediate fusion) on the model's accuracy and computational cost. Which of the following statements is generally TRUE regarding these fusion strategies?
Answer: D
Explanation:
Early fusion concatenates the input features early in the network, reducing computational complexity. However, it may not effectively capture modality-specific nuances. Late fusion combines modality-specific predictions, allowing for independent processing but potentially missing early interactions. Intermediate fusion offers a balance, but the optimal strategy depends on the specific task and data characteristics.
NEW QUESTION # 317
Consider a scenario where you are developing a multimodal model for medical diagnosis using patient medical history (text), X-ray images, and ECG data (time-series). A significant portion of the ECG data is missing due to sensor malfunction. Which of the following approaches would be MOST effective in handling the missing data and ensuring accurate diagnosis?
Answer: D
Explanation:
Combining imputation with robust fusion is optimal. Imputation recovers some information from the missing data, while robust fusion ensures the model can still make accurate predictions even if the imputed data is not perfect. Ignoring the ECG data or simply replacing it with average values would likely lead to inaccurate diagnoses.
NEW QUESTION # 318
You are tasked with deploying a generative A1 model trained with NeMo using Triton Inference Server. You want to leverage TensorRT for optimized inference. Which of the following steps is crucial to ensure compatibility and optimal performance?
Answer: B
Explanation:
Exporting the NeMo model to ONNX (Open Neural Network Exchange) is essential for compatibility with Triton Inference Server and TensorRT optimization. ONNX provides a standard format that TensorRT can ingest and optimize for efficient inference on NVIDIA GPUs.
NEW QUESTION # 319
You are designing an experiment to compare two different multimodal A1 model architectures for video summarization. Model A is a transformer-based model, and Model B is a recurrent neural network (RNN)-based model. Which of the following evaluation metrics would be MOST appropriate for comparing the quality of the generated summaries, considering both content relevance and fluency?
Answer: B
Explanation:
ROUGE is a recall-based metric that effectively measures the overlap between the generated summary and reference summaries. It's well-suited for evaluating the content relevance of summaries. BLEU, while used for text generation, focuses on precision and might penalize summaries with different wording but similar meaning. Perplexity measures fluency but not relevance. MSE is inappropriate for text. Inception score is used primarily for images.
NEW QUESTION # 320
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