![]() Comprehends both temporal and spatial elements of a video.Another use case is surveillance purposes. This method is useful for producing high-quality versions of archival material and/or medical materials that are uneconomical to save in high-resolution format. GAN-based method allows you to create a high-resolution version of an image through Super-Resolution GANs. A GAN consists of a generator and a discriminator that creates new data and ensures that it is realistic. Generative Adversarial Networks (GANs) are one of these methods. Generative AI uses various methods to create new content based on the existing content. Image Resolution Increase (Super-Resolution) Source 3: “FAE-GAN: facial attribute editing with multi-scale attention normalization” 4. An example of facial attribute manipulation This type of conversion can also be used for manipulating the fundamental attributes of an image (such as a face, see the figure below), colorize them, or change their style. One example of such a conversion would be turning a daylight image into a nighttime image. It involves transforming the external elements of an image, such as its color, medium, or form, while preserving its constitutive elements. ![]() Source 2: “Generating Synthetic Space Allocation Probability Layouts Based on Trained Conditional-GANs” 3. An example of semantic image-to-photo translation. Due to its facilitative role in making diagnoses, this application is useful for the healthcare sector. Semantic Image-to-Photo Translationīased on a semantic image or sketch, it is possible to produce a realistic version of an image. This AI-generated image was produced based on the text description of “Teddy bears shopping for groceries in ukiyo-e style”. An image generator, for example, can help a graphic designer create whatever image they need (See the figure below). It is also possible to use these visual materials for commercial purposes that make AI-generated image creation a useful element in media, design, advertisement, marketing, education, etc. Therefore, it is possible to generate the needed visual material in a quick and simple manner. With generative AI, users can transform text into images and generate realistic images based on a setting, subject, style, or location that they specify. General Applications of Generative AI > Visual Applications 1. prediction or classification), read our list of AI applications. For other applications of AI for requests where there is a single correct answer (e.g. We focused on real-world applications with examples but given how novel this technology is, some of these are potential use cases. In this article, we have gathered the top 100+ generative AI applications that can be used in general or for industry-specific purposes. Until 2025 AIMultiple expects generative AI to be responsible for a significant share of machine generated data and used to some degree in most of human generated data.Īs one of the most important strategic technology trends of 2023, this branch of artificial intelligence (AI) has a wide variety of applications that are useful to different industries and business functions, including:.Gartner predicts that by 2025, the percentage of data generated by generative AI will amount to 10% of all generated data.As you can see above, interest in generative AI exploded since October 2022 thanks to the launch of ChatGPT.Generative AI applications produce novel and realistic visual, textual, and animated content within minutes.
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