4/11/2023 0 Comments 771 aquared deep dreamer![]() The new ‘Nexus-6’ androids developed by the Tyrell corporation start to develop their own emotional responses over time, and the new prototype Rachel has had memory implants leading to her thinking that she is human. One of the overarching themes of the story is that the task of determining what is and isn’t human is becoming increasingly difficult, with the ever-increasing technological developments. Deckard has to issue Voight-Kampff tests in order to distinguish androids from humans, asking increasing difficult moral questions and inspecting the the subject’s pupils, with the intention of eliciting an empathic response in humans, but not androids. In the film Rick Deckard (Harrison Ford) is a bounty-hunter who makes a living hunting down and killing replicants - androids that are so well engineered that they are physically indistinguishable from human beings. Ridley Scott’s Blade Runner (1982) is the film adaption of the classic science fiction novel Do Androids Dream of Electric Sheep? by Phillip K. Reconstruction of the second Voight-Kampff test ![]() The network was trained for 6 epochs, taking about 2 weeks on my GPU. The network was trained on a dataset of all of the frames of Blade Runner cropped and scaled to 256x144. The latent representation has 200 variables, meaning the model is encoding a 256x144 image with 3 colour channels (110,592 variables) into a 200 digit representation, before reconstructing the image. The previous models described both modelled images at a resolution of 64圆4 with a batch size of 64, I scaled the network up to model images at a resolution of 256x144 with a batch size of 12 (the largest I could fit on my GPU - a NVIDIA GTX 960). It did however, lead me to building this model to generate large non-square images. Unfortunately due to time constraints I was not able to pursue this. I implemented the model in TensorFlow, with the intention of extending it with an LSTM in order to do video prediction. The discriminator processes the original and reconstructed data samples, assessing whether they are real or fake and the response in the higher layers of this network are compared to assess how similar the reconstruction is to the original sample. The decoder then attempts to reconstruct the data sample from the latent representation. The encoder encodes a data sample x into a latent representation z. Larsen et al.’s model consists of three separate networks, an encoder, a decoder and a discriminator. Overview of the variational autoencoder model combined with a discriminator network. published a paper that combined both of those approaches in a very elegant way by comparing the difference in response of the real and reconstructed samples in the higher layers of a discriminator network, they are able to produce a learned similarity metric that is far superior to a pixel-wise reconstruction error comparison (which otherwise leads to a blurred reconstruction - see Fig.2). But before I even had a chance to do that, Larsen et al. So I started investigating ways in which to train a variational autoencoder - which can reconstruct images - with the discriminator network that is used in the adversarial approach, or even some kind of network to assess how similar a reconstructed sample is to the real sample. However generative adversarial networks cannot reconstruct images, they only generate samples from random noise. I had been investigating generative models prior to Radford et al.’s paper, but when it was published it was obvious that this was the approach to follow. )įig.2 Comparison of VAE to VAE with learned similarity metric and GAN (If you are not familiar with what a convolutional neural network is, I made an online visualisation of one. did away with pooling layers entirely and simply used strided backwards convolutions. 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. The important breakthrough that made this possible was the use of a convolutional architecture for the generation of images. 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 adversarial method was first proposed by Goodfellow et al. Over time the generator becomes very good at producing realistic images that can fool the discriminator. 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. Fig.1 Images of bedrooms generated with DCGAN
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