This book chapter provides a comprehensive overview of generative AI and its applications in computer vision. The introduction section elucidates the concept of generative AI and underscores its importance within the realm of artificial intelligence. The chapter also provides a deep dive into the various techniques used in generative AI, such as creative style transfer, forecasting subsequent video frames, enhancing image resolution, enabling interactive image generation, facilitating image-to-image translation, text-to-image synthesis, image inpainting, the generation of innovative animated characters, the construction of 3D models from image, the utilization of the variational autoencoder (VAE) and its various adaptations, the implementation of generative adversarial networks (GANs) and their diverse iterations, as well as the use of transformers and their manifold versions. The chapter also highlights the current limitations and potential future developments in the field.
TopIntroduction
In the past few years, Generative Artificial Intelligence has become more and more seen as a major technology in computer vision. The name “Generative AI” signifies that machine learning algorithms are used to create new data which looks like existing datasets. It can possibly entirely change data production techniques, mill the art of computer vision, and offer new scope for a number of different disciplines all at once. Generative Artificial Intelligence, usually called Generative AI, is where technological innovation begins. This chapter will give you an overview of the field of generative artificial intelligence, describing its basic principles and the use of practical knowledge; we will also touch upon its considerable impact in all walks of life, thus providing an in-depth look into this new kind of science.
The reason of this chapter is to provide a clean, non-technical creation to Generative AI. The chapter has five elements, every exploring an exceptional feature of Generative AI. In Part One, we put forward the idea of Generative AI and its practical applications. Part Two briefly discusses literature on Generative AI. In Part Three, we observe VAEs, and GANs as a technique for enlargement of training datasets. To admire the notable works of Generative AI in numerous fields, the unrelentingly innovative roles played by its creators is delivered to life. This Chapter also examine the electricity of synthetic data and the way it can be used to enhance computer vision. Finally, in Section four, we explore Generative AI's challenges and destiny guidelines in computer vision.
This book chapter's intention is to offer an intensive expertise of Generative AI and its packages in computer vision. The cause of this chapter is to offer the perception of Generative AI and its feasible effect on Computer vision. It also examines numerous Generative AI approaches and their applications in Computer vision, together with artificial records generation, image enhancement, and image-to-image translation. In addition, the chapter digs into the literature perspectives of Generative AI in computer vision, which consist of several Generative AI approaches and their applications. It examines the significance of VAEs and GANs in increasing training datasets, in addition to the issues related to using artificial data in Computer vision.
Overall, the chapter attempts to offer readers with a thorough grasp of generative AI and its packages in pc vision, similarly to its potential effect in the place and the hurdles that need to be triumph over on the way to absolutely comprehend its promise. It is supposed for researchers, practitioners, and college students who are interested by generative AI and its implications for computer vision and distinct domains. In the hastily evolving subject, AI enablement has emerged as a breakthrough with the capacity to convert the manner we generate and eat data. This volume is an introductory adventure into the world of generative AI, exploring key standards and applications and taking a better observe critical generative paradigms—generative Adversarial networks (GANs) and variational autoencoders (VAEs). Generative AI, at its core, represents a paradigm shift in AI from clearly recognizing styles in cutting-edge facts to actively growing new data styles. Based amongst deep reading and probabilistic reasoning approaches, it harnesses the electricity of neural networks to investigate and reconstruct complicated information classifications.
Within this chapter, we're going to delve into the mechanics of Generative AI, exploring its importance and capacity via the lens of key generative models—VAEs and GANs —which have played instrumental roles in shaping the landscape of records era and manipulation.
Generative AI, a form of artificial intelligence, has the ability to procedure a variety of items inclusive of textual content, photographs, audio, and synthetic facts The latest surge in interest in generative AI may be attributed to person-friendly interfaces a characteristic that has made it clean to create excessive- first-class text, snap shots and video in seconds.
While generative AI isn't a latest innovation, it has made great strides in recent years. Its origins date back to the 1960s, when chatbots first introduced the concept. However, it was not until 2014 that anti-generational networks (GANs), a set of machine learning algorithms, were introduced. Using GANs, the AI-enabled system could create virtual reality images, videos, and audios, including those of individuals themselves.