Autoencoding Blade Runner
Generative models for images have come a long way in recent years. This talk recalls the creative exploration of an unconventional use of a convolutional autoencoder trained with a learned similarity metric. By using all the frames from Blade Runner as the training dataset for this model, we have a generative model that has learned the distribution of scenes from Blade Runner. We use this model to reinterpret the film, and to reinterpret other films based on its understanding of Blade Runner.
Terence is an artist and research engineer at Goldsmiths, where he recently completed his Masters in Creative Computing. His focus has been on the generative capabilities and creative applications of deep learning, developing an interactive topological visualisation of a convolutional neural network, and remaking the film Blade Runner with a convolutional autoencoder.