Metapix is using below AI algorithms to generate creative Art

Here are 12 AI algorithms that are commonly used for generating creative images:

1. Generative Adversarial Networks (GANs) – GANs are a type of deep learning algorithm that can generate realistic images by training on a large dataset of real images. They consist of two neural networks: a generator network that creates new images, and a discriminator network that determines whether an image is real or fake.
2. Variational Autoencoders (VAEs) – VAEs are a type of deep learning algorithm that can generate new images by learning the underlying distribution of a dataset of real images. They consist of an encoder network that compresses an input image into a latent space, and a decoder network that reconstructs the image from the latent space.
3. Deep Convolutional Generative Adversarial Networks (DCGANs) – DCGANs are a variant of GANs that use convolutional neural networks (CNNs) as the generator and discriminator networks. They are particularly effective at generating images with high resolution and fine details.
4. Style Transfer – Style transfer algorithms use deep learning to transfer the style of one image to another image, while preserving the content of the second image. This can be used to create artistic effects or to mimic the style of a particular artist or photographer.
5. Neural Style Transfer – Neural style transfer is a type of style transfer algorithm that uses a CNN to learn the style of a reference image and apply it to a target image. It can generate high-quality images with complex styles, such as those produced by famous artists or photographers.
6. CycleGAN – CycleGAN is a type of GAN that can translate images from one domain to another, such as converting photos of horses to photos of zebras or vice versa. It can also be used to transfer the style of one image to another, such as converting a photo to a painting.
7. Pix2Pix – Pix2Pix is a type of GAN that can generate images from structured input data, such as a sketch or a map. It can be used to generate realistic images of objects, scenes, or people from rough sketches or outlines.
8. DeepDream – DeepDream is an algorithm that uses a CNN to generate images based on the features learned by the network. It can be used to create surreal or abstract images by manipulating the features learned by the network.
9. Deep Convolutional Inverse Graphics Network (DC-IGN) – DC-IGN is an algorithm that uses a CNN to generate images from structured input data, such as 3D models or sketches. It can generate high-quality images with complex details and realistic lighting and shading.
10. Neural Evolution – Neural evolution is an algorithm that uses a genetic algorithm to evolve a neural network for generating images. It can be used to discover novel image generation techniques or to find solutions to specific image generation tasks.
11. Neural Colorization – Neural colorization is an algorithm that uses a CNN to colorize black and white photos. It can generate realistic and accurate colorizations, even for images with complex lighting and shading.
12. Neural Tone Mapping – Neural tone mapping is an algorithm that uses a CNN to adjust the exposure and contrast of images, similar to how a human eye adjusts to different lighting conditions. It can be used to improve the visual quality of images or to create artistic effects.