Various tools and methods are available to create images using artificial intelligence. The creation of images with the intervention of AI is a source of ethical questions. Legality is also an issue that comes up frequently, particularly in terms of copyright and the use of personal data. Find out how AI generates images while enabling users to explore new styles. It also makes it possible to automate certain creative tasks.
The impact of the rise of artificial intelligence on image creation
The rise of artificial intelligence has had an astonishing impact on image creation. It has opened up a whole new world of creative possibilities. The techniques deployed by AI make it possible to generate realistic images from databases. Depending on its programming, AI can learn from heterogeneous sets. It then reproduces patterns, textures and details with great precision.
It can adapt to the evolution of different systems. AI makes it possible to automate certain creative design tasks. For example, it can automatically create landscapes, objects and even faces. This saves visual content creators a lot of time. Designers are free to explore new horizons thanks to AI.
Artificial intelligence offers new ways of interacting with images. Indeed, it’s easier to search for similar content and modify certain details in real time. This increases the opportunities for visual creation.
The different technologies for generating images via artificial intelligence
There are two types of technology for generating images using artificial intelligence. Image generation based on generative adversarial neural networks (GANs) and image generation based on convolutional neural networks (CNNs).
Generative adversarial neural networks GAN
GANs (Generative Adversarial Networks) are so-called deep neural networks. They consist of two parts. The generator part produces synthetic images via random noise. The discriminator part takes care of distinguishing the images produced by the generator from real images, downloaded from the Web. The aim of the generator is to gain the skill to mislead the discriminator. In this way, contrasting the two parts of the GAN produces increasingly realistic visual content.
Convolutional neural networks (CNNs)
CNNs (Convolutional Neural Networks) are specialized in image classification, but also in image generation. Convolutional neural networks are trained on real images. These are modified to create new images via residual blocks, normalization layers or transposed layers.
The AI image creation process
There are several stages involved in generating visual content. The first is the collection of training data. In fact, the AI needs a fairly diversified pool of data to train itself to produce content. In general, this pool is made up of images that already exist. A great deal of upstream work is carried out by the AI’s programmers.
The choice of AI model depends essentially on the programmers’ objective. They choose between generative adversarial neural networks (GANs) and convolutional neural networks (CNNs). As already mentioned, these are the two most common architectures.
Once the model has been selected, the AI needs to be trained so that the designers can fine-tune the various parameters. It is evaluated to check its ability to produce quality visual content. Evaluation metrics are used as benchmarks. Adjustments are made throughout the process to improve AI performance.
Once the model has been sufficiently tested, it is considered fit for service. Users can then use the AI to produce visual content. They provide a specific input: random noise, text prompt, etc. The AI uses its learning to produce the image the user is looking for. Filters can be applied to precisely define certain characteristics of the images generated.
