Discover the well-established architectures used in Generative AI, including VAEs, GANs, Diffusion Models, Transformers, and NeRFs. Prepare for the NVIDIA Generative AI Explained certification exam with this comprehensive overview.
Table of Contents
Question
Unlike discriminative artificial intelligence that performs classification tasks, modern Generative Artificial Intelligence uses machine learning and deep neural networks to understand and conditionally generate new examples from complex data distributions. What are some well-established architectures used to develop Generative AI?
A. Embeddings to represent high-dimensional, complex data
B. Variational Autoecoders (VAE) use an encoder-decoder architecture to generate new data, typically for image and video generation
C. Generative Adversarial Networks (GAN) use a generator and discriminator to generate new data, often in video generation
D. Diffusion Models add and remove noises to generate quality images with high levels of detail
E. Transformers for Large Language Models such as GPT, LaMBDA, and LLaMa
F. Neural Radiance Fields (NeRF) for generating 3D content from 2D images
Answer
A. Embeddings to represent high-dimensional, complex data
B. Variational Autoecoders (VAE) use an encoder-decoder architecture to generate new data, typically for image and video generation
C. Generative Adversarial Networks (GAN) use a generator and discriminator to generate new data, often in video generation
D. Diffusion Models add and remove noises to generate quality images with high levels of detail
E. Transformers for Large Language Models such as GPT, LaMBDA, and LLaMa
F. Neural Radiance Fields (NeRF) for generating 3D content from 2D images
Explanation
All of the options listed are well-established architectures used to develop Generative AI:
A. Embeddings are used to represent high-dimensional, complex data in a more compact and meaningful way, enabling AI models to better understand and generate new examples.
B. Variational Autoencoders (VAEs) use an encoder-decoder architecture to learn a compressed representation of input data, which can then be sampled from to generate new data, typically for image and video generation.
C. Generative Adversarial Networks (GANs) consist of a generator and discriminator network that compete against each other, with the generator learning to create realistic new data that can fool the discriminator, often used in video generation.
D. Diffusion Models generate high-quality images with fine details by gradually adding and removing noise to an initial random noise image, guided by a learned denoising process.
E. Transformers have revolutionized natural language processing and are the backbone of large language models like GPT, LaMDA, and LLaMA, enabling powerful language understanding and generation capabilities.
F. Neural Radiance Fields (NeRFs) learn an implicit 3D scene representation from a set of 2D images, allowing for the generation of novel 3D views and content.
These architectures form the foundation of modern Generative AI, enabling machines to understand and create new examples across various domains, including images, videos, text, and 3D content.
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