GEN AI Solution Competition 2023

📍University of Southern California /
Information Sciences Institute(ISI), USA
December 15, 2023

Generative Adversarial Networks (GANs)

This model is best for image duplication and synthetic data generation.

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Transformer-based Models

This model is best for text generation and content/code completion. Common subsets of transformer-based models include generative pre-trained transformer (GPT) and bidirectional encoder representations from transformers (BERT) models.

Diffusion Models

This model is best for image generation and video/image synthesis.

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Variational Autoencoders (VAEs)

This model is best for image, audio, and video content creation, especially when synthetic data needs to be photorealistic; designed with an encoder-decoder infrastructure.

Unimodal Models

This model is that are set up to accept only one data input format; most generative AI models today are unimodal models.

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Multimodal Models

This model is designed to accept multiple types of inputs and prompts when generating outputs; for example, GPT-4 can accept both text and images as inputs.

Large Language Models

This model is the most popular and well-known type of generative AI model right now,large language models(LLMs) are designed to generate and complete written content at scale.

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Neural Radiance Fields (NeRFs)

This model is an emerging neural network technology that can be used to generate 3D imagery based on 2D image inputs.