An AI image generator, also known as a generative model, is an artificial intelligence system designed to create new images based on a set of input parameters or conditions. These systems use machine learning (ML) algorithms that can learn from large datasets of images, allowing them to generate new images that are similar in style and content to the original dataset.
The main advantage of AI image generators is that they can create images without human intervention, which can save time and resources in many industries. For example, in the fashion industry, AI image generators can be used to create clothing designs or style outfits without the need for human designers. In the gaming industry, AI image generators can create realistic characters, backgrounds, and environments that would have taken months to create manually.
There are different types of AI image generators, each with its own set of strengths and weaknesses. Some of the most popular types of AI image generators include style transfer, which allows users to transfer the style of one image onto another, and GANs (Generative Adversarial Networks), which use two neural networks to generate realistic images that resemble the original dataset. Regardless of the type, AI image generators have immense potential to revolutionize how we create and consume visual content. Continue reading from Unite AI
GANs are computational structures that pit two neural networks against one another (hence, the name "adversarial") to generate new, synthetic examples of data that can pass for real data. They're generally implemented in picture, video, and voice creation.
Generative adversarial networks are divided into three parts:
1. Generative: A generative model specifies how data is created in terms of a probabilistic model.
2. Adversarial: The model is trained in an adversarial environment.
3. Networks: Deep neural networks, which are artificial intelligence (AI) systems, are used for training.
A generator and a discriminator are both present in GANs. The generator creates fake data samples (images, audio, etc.) to deceive the discriminator. On the other hand, the discriminator seeks to discriminate between actual and fraudulent samples.
Both the generator and the discriminator are neural networks and they compete with each other throughout the training phase. The procedures are performed multiple times and with each iteration, the generator and discriminator improve their performance in their respective roles. Continue reading from Turing
3 Questions: How AI image generators work (MIT College of Computing)
A beginner’s guide to AI Style Transfer (Manipal)
Generative Adversarial Networks: A gentle introduction for beginners (Medium)
Neural style transfer (TensorFlow)
The best AI art generators: DALL-E 2 and fun alternatives to try (ZD Net)