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Meow Generator

@machinelearnbot

I experimented with generating faces of cats using Generative adversarial networks (GAN). So I doubt that training a cat generator with 5 layers and 128 hidden nodes would be much of a problem. LSGAN is a slightly different approach where we try to minimize the squared distance between the Discrimination output and its assigned label; they recommend using: 1 for real images, 0 for fake images in Discriminator update and then 1 for fake images in Generator update.


Is Artificial Intelligence Set To Become Art's Next Medium? - South Florida Reporter

#artificialintelligence

The painting, if that is the right term, is one of a group of portraits of the fictional Belamy family created by Obvious, a Paris-based collective consisting of Hugo Caselles-Dupré, Pierre Fautrel and Gauthier Vernier. They are engaged in exploring the interface between art and artificial intelligence, and their method goes by the acronym GAN, which stands for'generative adversarial network'. 'The algorithm is composed of two parts,' says Caselles-Dupré. 'On one side is the Generator, on the other the Discriminator. We fed the system with a data set of 15,000 portraits painted between the 14th century to the 20th.


GAN Pix2Pix Generative Model

#artificialintelligence

We hear a lot about language translation with deep learning where the neural network learns a mapping from one language to another. In fact, Google translate uses one to translate to more than 100 languages. But, can we do a similar task with images? If it's possible to capture the intricacies of languages, it'll surely be possible to translate an image to another. Indeed, this shows the power of deep learning.


Hands-On Guide To Deep Convolutional GAN for Fashion Apparel Image Generation

#artificialintelligence

Generative Adversarial Networks (GANs) are a trend nowadays in various unsupervised learning applications. They are applied in animation and gaming with a full swing due to their capability to produce new images when trained on a set of similar but different images. This model is basically a deep generative model composed of two networks – a generator and a discriminator. The Deep Convolutional Neural Network is one of the variants of GAN where convolutional layers are added to the generator and discriminator networks. In this article, we will train the Deep Convolutional Generative Adversarial Network on Fashion MNIST training images in order to generate a new set of fashion apparel images.


IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis

arXiv.org Machine Learning

We present a novel introspective variational autoencoder (IntroVAE) model for synthesizing high-resolution photographic images. IntroVAE is capable of self-evaluating the quality of its generated samples and improving itself accordingly. Its inference and generator models are jointly trained in an introspective way. On one hand, the generator is required to reconstruct the input images from the noisy outputs of the inference model as normal VAEs. On the other hand, the inference model is encouraged to classify between the generated and real samples while the generator tries to fool it as GANs. These two famous generative frameworks are integrated in a simple yet efficient single-stream architecture that can be trained in a single stage. IntroVAE preserves the advantages of VAEs, such as stable training and nice latent manifold. Unlike most other hybrid models of VAEs and GANs, IntroVAE requires no extra discriminators, because the inference model itself serves as a discriminator to distinguish between the generated and real samples. Experiments demonstrate that our method produces high-resolution photo-realistic images (e.g., CELEBA images at \(1024^{2}\)), which are comparable to or better than the state-of-the-art GANs.