Huawei H13-311_V3.5 Exam With Confidence Using Practice Dumps

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.

Activation functions like Sigmoid, tanh, and softsign suffer from the vanishing gradient problem when used in deep networks. This happens because, in these functions, gradients become very small as the input moves away from the origin (either positively or negatively). As a result, the weights of the earlier layers in the network receive very small updates, hindering the learning process in deep networks. This is one reason why activation functions like ReLU, which avoid this issue, are often preferred in deep learning.

Huawei Cloud EI (Enterprise Intelligence) provides a variety of AI services and platforms, including knowledge graph, OCR (Optical Character Recognition), machine translation, and the Celia virtual assistant development platform. These services enable businesses to integrate AI capabilities such as language processing, image recognition, and virtual assistant development into their systems.