In the following image, the ´code´ section refers to the method used for the compression. VAE differs from common autoencoders by the method it uses to compress data, via a multivariate latent distribution. Generative Adversarial Networks (GANs) – Image source Variational autoencoder (VAE)Īn autoencoder is a neural network that compresses data in an attempt to reconstruct it from the resulting representation. With this method, the algorithm selects the images that seem more “real”, meaning that it is more similar to the original data. ![]() The generator aims to generate new images, and the discriminator classifies them as “real” or “fake”. Generative modeling refers to an unsupervised learning method that automatically discovers patterns in inputs, that are then used to generate similar outputs. GANs reach the generative model by dividing the problem into 2 networks the generator and the discriminator. There are different types of generative models available, and here we will break down the most popular for generating high-quality and innovative results. The work of a generative model involves the distribution of data to see how likely a given example is. Interested in working with AI technologies? We’re hiring! Generative Models How to change styles of original images.To bring you quickly up to speed we will explore the following in this article: These types of AI-powered models can help with generating images for blogs, album covers, printing wall paintings for our room or desk, generating NFTs, and much more! To all who may be interested, did you know that you can generate images using AI? It’s true, and this might be helpful for you in so many fun ways.
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