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NEW QUESTION # 381
You are experimenting with a multimodal model that takes both text and audio as input. During evaluation, you notice that the model is heavily biased towards the text input, largely ignoring the audio. Which of the following techniques could you employ to mitigate this modality imbalance and encourage the model to effectively utilize both inputs? (Select all that apply)
Answer: A,C
Explanation:
Modality imbalance is a common issue in multimodal learning. Applying modality-specific dropout to the dominant modality (text, in this case) forces the model to rely more on the other modality (audio). A contrastive loss directly encourages the model to learn aligned representations between the two modalities. Increasing the audio encoder's learning rate (A) might help, but it is less targeted than dropout or contrastive loss. Reducing the text encoder size (D) is unlikely to be helpful in a controlled way. Replacing Audio features with raw waveform might introduce noise.
NEW QUESTION # 382
You are experimenting with different multimodal transformer architectures for a video understanding task. You are using a large pre- trained model and fine-tuning it on your specific dataset. You observe that the model is overfitting and struggling to generalize to unseen videos. Which of the following techniques would be most effective in mitigating overfitting in this scenario? (Choose two)
Answer: B,C
Explanation:
Weight decay and dropout are standard regularization techniques that help prevent overfitting. Data augmentation increases the diversity of the training data, improving the model's ability to generalize. Reducing the number of layers is a potentially viable option, but requires experimentation to achieve optimum performance.
NEW QUESTION # 383
Consider the following Python code snippet using PyTorch, intended to combine image and text embeddings:
Which of the following statements regarding the output shapes of these combined embeddings are TRUE? (Select TWO)
Answer: D,E
Explanation:
torch.cat concatenates the embeddings along dimension 1, resulting in shape (32, 1024). Element-wise addition maintains the original shape (32, 512). The weighted sum is also element-wise, preserving the (32, 512) shape.
NEW QUESTION # 384
You are building a multimodal generative model that combines text and images. The goal is to generate realistic images based on textual descriptions. You have access to a pre-trained language model (e.g., BERT) and a pre-trained image generation model (e.g., StyleGAN). Which of the following architectures would be MOST suitable for effectively integrating these two models to achieve your objective?
Answer: B
Explanation:
Using the language model to generate a latent vector that serves as input to the image generation model is an effective approach for multimodal integration. This allows the language model to encode the textual description into a meaningful representation that can guide the image generation process. Fine-tuning the language model to output pixel values directly is not feasible due to the high dimensionality of images. Training a separate network to map images to text is a reverse task. Concatenating text and image data may not effectively capture the complex relationships between modalities. Generating captions for images is not the primary objective.
NEW QUESTION # 385
In the context of multimodal data analysis, which of the following statements accurately describe the challenges associated with data alignment?
Answer: B,E
Explanation:
Data alignment is crucial for ensuring that information from different modalities is correctly associated with the same event or entity. Misalignment can lead to incorrect relationships being learned by the model, resulting in poor performance. Data alignment is necessary for various types of multimodal data, not just time-series data. Perfect alignment is often difficult to achieve due to inherent noise and limitations in data collection. Deep learning models are also susceptible to issues caused by data misalignment.
NEW QUESTION # 386
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