Generative Artificial Intelligence Models: A Survey
This paper discusses how Generative AI has advanced to create new content like text and images. It explains the different models used in this field and their applications.
This video presentation explains the key concepts from the paper in plain language.
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- 1 Each paradigm differs significantly in its optimization objective, likelihood formulation, training stability, and computational complexity.
- 2 Future research should aim not only at improving generative performance, but also at enhancing fairness, transparency, controllability, and computational sustainability.
- 3 These models perform stochastic sampling to synthesize novel, high-fidelity instances that are statistically consistent with the original inputs.
- 4 A variety of survey studies have emerged to provide researchers, developers, and practitioners with a deeper understanding of GenAI models.
Introduction
Generative Artificial Intelligence (GenAI) uses Artificial Intelligence (AI) to create novel content such as text, images, audio, or videos. Simple chatbots using rulebased techniques appeared in the 1960s as the earliest attempts at generating new content.
Early systems like ELIZA relied on pattern matching and substitution templates that lacked a true probabilistic understanding of data.
The evolution of GenAI is closely linked to continuous advancements in Neural Network (NN) architectures.
BERT is limited in its ability to perform generative tasks and typically requires fine-tuning for tasks.
Research Question
Each paradigm differs significantly in its optimization objective, likelihood formulation, training stability, and computational complexity. By learning a lowerdimensional, perceptually equivalent latent space with an autoencoder, the diffusion process can operate much more efficiently, leading to faster sampling and training while maintaining high image quality. u2022 Consistency Models: These models aim to distill the multi-step sampling process of diffusion models into a single-step or few-step generation process, improving inference speed.
Future research should aim not only at improving generative performance, but also at enhancing fairness, transparency, controllability, and computational sustainability.
Methodology
GenAI models approximate the high-dimensional probability distribution p(x) of a dataset rather than learning a functional mapping f (x) u2192 y for classification or regression. Some surveys provide an in-depth analysis of individual model families such as Transformers, GANs, VAEs, or DMs.
Study Design
This paper presents a structured, cross-paradigm analysis of dominant GenAI architectures, emphasizing comparative insights across theoretical formulations, training dynamics, and practical constraints.
Our objective is to support researchers, students, and industry practitioners by providing a clear comparative analysis of major generative models and their variants.
An in-depth analysis of the complexities of these other models lies beyond the scope of this survey.
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Results & Findings
These models perform stochastic sampling to synthesize novel, high-fidelity instances that are statistically consistent with the original inputs. Various NN configurations and training methodologies produce unprecedented creative and generative capabilities that push the boundaries of what AI can achieve.
- These models perform stochastic sampling to synthesize novel, high-fidelity instances that are statistically consistent with the original inputs.
- Various NN configurations and training methodologies produce unprecedented creative and generative capabilities that push the boundaries of what AI can achieve.
- A variety of survey studies have emerged to provide researchers, developers, and practitioners with a deeper understanding of GenAI models.
- Few works attempt to provide comprehensive overviews of GenAI, but they often lack coverage of recent developments and emerging model variants.
- The literature still lacks a unified, comprehensive survey that offers a holistic understanding of the full spectrum of GenAI models.
There are wide range of applications of GANs including image editing; image translations; text translations; audio enhancement; improving cybersecurity by detecting security anomalies, assisting in password cracking, and producing more Compressed, fast inference Depends on teacher model, weaker in complex.
DMs demonstrate significant potential over different disciplines, in the medical field for improving diagnostics and treatment to reconstructing high-quality medical images from incomplete or noisy data, or generating synthetic medical images to augment limited real datasets for training other AI.
Practical Applications
The generation of highly authentic human images and sounds by AI became possible in 2014 following the advent of Generative Adversarial Networks (GANs). The GPT architecture was adapted for image generation tasks, demonstrating that it could be applied beyond text to generate coherent images from pixel-level data.
GAN may be used in producing realistic and captivating visual experiences in video games and animation, or to modify images such as turning a picture in black-and-white to colored one or a low-resolution image to a high-resolution image.
This may cause issues such as mode collapse, where the generator produces a limited variety of outputs, ignoring many possible modes of the true data distribution.
This may cause issues such as mode collapse, where the generator produces a limited variety of outputs, ignoring many possible modes of the true data distribution.
Transformers
Introduced in 2017, Transformers replaced recurrence and convolution with self-attention mechanisms, allowing for parallel computation and effective modeling of long-range dependencies in sequences.
Transformer main architecture's components
The Transformer architecture consists of an encoder and decoder, utilizing multi-head self-attention and feed-forward layers to capture relationships among tokens, with trade-offs in time and memory complexity.
Figures Explained
Frequently Asked Questions
Each paradigm differs significantly in its optimization objective, likelihood formulation, training stability, and computational complexity. Future research should aim not only at improving generative performance, but also at enhancing fairness, transparency, controllability, and computational sustainability.
Our objective is to support researchers, students, and industry practitioners by providing a clear comparative analysis of major generative models and their variants. Basically, GANs are trained using a game-theoretic method in which the discriminator seeks to minimize error and the generator.
These models perform stochastic sampling to synthesize novel, high-fidelity instances that are statistically consistent with the original inputs. A variety of survey studies have emerged to provide researchers, developers, and practitioners with a deeper understanding of GenAI models.
The GPT architecture was adapted for image generation tasks, demonstrating that it could be applied beyond text to generate coherent images from pixel-level data. These are types of GANs which consider additional conditioning information y during the generation process, where y could.
There are wide range of applications of GANs including image editing; image translations; text translations; audio enhancement; improving cybersecurity by detecting security anomalies, assisting in password cracking, and producing more Compressed, fast inference Depends on teacher model, weaker in complex tasks MobileBERT.
This paper discusses how Generative AI has advanced to create new content like text and images. It explains the different models used in this field and their applications.