Let's turn our brain structures into code. The invention of deep learning by Geoffrey Hinton in 2006 [1] opened various research possibilities. He is also inventor of the backpropagation. The deep layered structure resembles the cortex zones of the human brain. But the more layers in the neural network are presented, the more complex it is to train.

The Pix2Pix GAN is a generator model for performing image-to-image translation trained on paired examples. For example, the model can be used to translate images of daytime to nighttime, or from sketches of products like shoes to photographs of products. The benefit of the Pix2Pix model is that compared to other GANs for conditional image generation, it is relatively simple and capable of generating large high-quality images across a variety of image translation tasks. The model is very impressive but has an architecture that appears somewhat complicated to implement for beginners. In this tutorial, you will discover how to implement the Pix2Pix GAN architecture from scratch using the Keras deep learning framework. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code. How to Implement Pix2Pix GAN Models From Scratch With Keras Photo by Ray in Manila, some rights reserved.

Using Pose Estimation and Conditional Adversarial Networks to create and visualize new Fortnite dances. If you know about the game Fortnite, you probably also know about the craze surrounding the in-game celebrations/emotes/dances. Gamers have spent millions of dollars purchasing dance moves with in-app purchases, making something as simple and as silly as this a big revenue generator for the game developer. This got me thinking, if the developer allowed the users to create these dances in the game and charged extra for it, they can probably make more money. As for the users, it would be really cool if we could record ourselves on a webcam and create our own celebratory dance within the game.

Using Pose Estimation and Conditional Adversarial Networks to create and visualize new Fortnite dances. If you know about the game Fortnite, you probably also know about the craze surrounding the in-game celebrations/emotes/dances. Gamers have spent millions of dollars purchasing dance moves with in-app purchases, making something as simple and as silly as this a big revenue generator for the game developer. This got me thinking, if the developer allowed the users to create these dances in the game and charged extra for it, they can probably make more money. As for the users, it would be really cool if we could record ourselves on a webcam and create our own celebratory dance within the game.

We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations… As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either. Pix2pix can produce effective results with way fewer training images, and much less training time, than I would have imagined.