Which of the following are use cases of generative adversarial networks?
Options:
A.
Photo repair
B.
Generating face images
C.
Generating a 3D model from a 2D image
D.
Generating images from text
Answer:
A, B, C, D
Explanation:
Generative Adversarial Networks (GANs) are widely used in several creative and image generation tasks, including:
A. Photo repair: GANs can be used to restore missing or damaged parts of images.
B. Generating face images: GANs are known for their ability to generate realistic face images.
C. Generating a 3D model from a 2D image: GANs can be used in applications where 2D images are converted into 3D models.
D. Generating images from text: GANs can also generate images based on text descriptions, as seen in tasks like text-to-image synthesis.
All of the provided options are valid use cases of GANs.
HCIA AI References:
Deep Learning Overview: Discusses the architecture and use cases of GANs, including applications in image generation and creative content.
AI Development Framework: Covers the role of GANs in various generative tasks across industries.
Question 18
In machine learning, which of the following inputs is required for model training and prediction?
Options:
A.
Neural network
B.
Historical data
C.
Training algorithm
D.
Manual program
Answer:
B
Explanation:
In machine learning, historical data is crucial for model training and prediction. The model learns from this data, identifying patterns and relationships between features and target variables. While the training algorithm is necessary for defining how the model learns, the input required for the model is historical data, as it serves as the foundation for training the model to make future predictions.
Neural networks and training algorithms are parts of the model development process, but they are not the actual input for model training.
