H13-311_V3.5 Exam Dumps : HCIA-AI V3.5 Exam

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.

In a convolutional neural network (CNN), each kernel (also called a filter) in the same convolutional layer does not share weights with other kernels. Each kernel is independent and learns different weights during training to detect different features in the input data. For instance, one kernel might learn to detect edges, while another might detect textures.

However, the same kernel’s weights are shared across all spatial positions it moves across the input feature map. This concept of weight sharing is what makes CNNs efficient and well-suited for tasks like image recognition.

Thus, the statement that all kernels share weights is false.

HCIA AI References:

Deep Learning Overview: Detailed description of CNNs, focusing on kernel operations and weight sharing mechanisms within a single kernel, but not across different kernels.

The pad_mode parameter in mindspore.nn.Conv2d() can take values such as:

same: Ensures the output has the same spatial dimensions as the input.

valid: Performs convolution without padding, resulting in an output smaller than the input.

Other values like "pad" and "nopadding" are not valid options for the pad_mode parameter.