What Are Diffusion Models?ĭiffusion models are a type of probabilistic generative model that has in recent times surpassed GANs in generating novel image renderings. Combined with instability during training and the added complexity of having two neural networks in competition with each other, the difficulty of using GANs has prevented them from dominating the competition. GANs can be used to create anything from photo-realistic images to artwork, and everything in between.ĭue to the duality of the generator versus discriminator setup, GANs often need more computing power to both train and use effectively. Over the course of training, the discriminator gets better at detecting fake images while the generator gets better at creating images that fool the discriminator. Normally GANs are made up of a generator that creates images and a discriminator, which tries to tell the different between training images and images from the generator network. Generative adversarial networks (GANs) are a system of neural networks that compete against each other. Synonyms for artistic style transfer include: "deep style" and just simply "style transfer". Since then subsequent research has greatly expanded and improved our knowledge of artistic style transfer. The idea originates from a research paper titled A Neural Algorithm of Artistic Style, which was published in August of 2015. Specifically, CNNs using a Visual Geometry Group (VGG) architecture have been found to work the best for artistic style transfer. What Is Artistic Style Transfer?Īrtistic style transfer utilizes convolutional neural networks (CNNs) to recreate an input image (content image) in the style of one or more style inputs (style images). In machine learning, DeepDream can be used to both examine a trained neural network model and to speed up the training of a neural network model. This creates a hallucinogenic type effect which resembles dream-like hallucinations, which sometimes resemble the effects of hallucinogenic drugs. What is DeepDream?ĭeepDream uses a sort of algorithmic pareidolia to see and then enhance patterns in an image. As the largest online AI art community, we routinely push the bounds of technology in the pursuit of better-looking artwork. We here at /r/DeepDream mainly focus on applications of deep learning which itself is a sub field of machine learning. Advances in the machine learning sub field of artificial intelligence brought on by the information age have made it possible for machines to create art that rivals that of what a human being can do. Others have set up sites where the less technically savvy of us can upload any photo and have it processed by Deep Dream.We are a community dedicated to art produced with the help of artificial neural networks, which are themselves inspired by the human brain. Now Google has released the code for this iterative process, nicknamed “Deep Dream.” People with enough know-how to manipulate that code are already doing some pretty cool things with it, like feeding it the movie Fear And Loathing In Las Vegas. And when the team let the network scan and enhance the same image for multiple iterations, all sorts of creepy stuff started to appear – eyes, animals, mouths… it basically looked like an acid trip gone very wrong. But it also has a serious case of Pareidolia – which for humans manifests itself as stuff like seeing animals in clouds and Jesus in a piece of burnt toast.įor Google’s artificial neural network, it sees and enhances all sorts of things in photos that aren’t really there. The programmers feed in images of different things, and the AI slowly learns to identify them in other photos it sees. A couple of weeks ago, Google’s artificial neural networks research team showed off some of its work in teaching a computer to recognize what it was looking at.
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