Autoencoding Blade Runner
A detailed article on a project to use artificial neural networks to build films, by training them on individual frames, and then getting them to reconstruct missing bits. AKA an autoencoder making cool videos with Blade Runner.
“In the past 12 months, interest in—and the development of — using artificial neural networks for the generation of text, images and sound has exploded. In particular, methods for the generation of images have advanced remarkably in recent months.
In November 2015, Radford et al. blew away the machine learning community with an approach of using a deep neural network to generate realistic images of bedrooms and faces using an adversarial training method in which a generator network generates random samples, and a discriminator network tries to determine which images are generated and which are real. Over time the generator becomes very good at producing realistic images that can fool the discriminator. The adversarial method was first proposed by Goodfellow et al. in 2013, but until Radford et al.’s paper, it hadn’t been possible to generate coherent and realistic natural images using neural nets. The important breakthrough that made this possible was the use of a convolutional architecture for the generation of images. Before this it had been assumed convolutional neural nets could not be used effectively for the generation of images, as the use of pooling layers lost spatial information between layers. Radford et al. did away with pooling layers entirely and simply used strided backwards convolutions. (If you are not familiar with what a convolutional neural network is, I made an online visualisation of one.)”
Read the whole article by Terry Broad
