NVIDIA Generative AI Multimodal - NCA-GENM 模擬練習

A. Manually labeling each image in the dataset for precise classification.

B. Focusing on enhancing the resolution and quality of images before classification.

C. Using a model like CLIP for encoding both images and their textual descriptions into a shared representation space for comparison.

D. Designing an algorithm to replace the need for textual descriptions in the classification process.

A. It is not suitable for deep neural networks.

B. It can cause dead neurons.

C. It is prone to vanishing gradient problem.

D. It is computationally expensive.

A. The model's accuracy in recognizing individuals of different races.

B. The model's ability to recognize various facial expressions.

C. The model's processing speed in recognizing faces of different races.

D. The model's compatibility with different operating systems.

A. Adjusting the weights and biases of a neural network to optimize its performance.

B. Selecting the optimal values for non-trainable parameters, such as learning rate or batch size.

C. Collecting and preprocessing data to improve the accuracy of the model.

D. Selecting the best ML algorithm for a given task.

A. In a multimodal context, usually, contrastive learning increases the similarity of representations across modalities for the different objects and decreases the similarity of representations across modalities for same objects.

B. Contrastive learning is a technique used to train deep learning models by comparing similar and dissimilar inputs and optimizing the model to maximize the similarity between representations of similar inputs and minimize the similarity between representations of dissimilar inputs.

C. Contrastive learning is a technique used to manipulate and analyze multimodal data using Generative AI.

D. In a multimodal context, usually, contrastive learning decreases the similarity of representations across modalities for the same objects and increases the similarity of representations across modalities for different objects.

E. In a multimodal context, usually, contrastive learning increases the similarity of representations across modalities for the same objects and decreases the similarity of representations across modalities for different objects.

A. It refers to the process of combining multiple modalities at the preprocessing stage.

B. It refers to the process of combining multiple modalities at the training stage.

C. It refers to the process of combining multiple modalities at the feature level.

D. It refers to the process of combining multiple modalities at the decision level.