… is neural network based discriminator, takes as input the real x, or x = G(z) generated by the
generator and classifies them as fake … is utilized to automatically search for a new conditional …

… ) methods, here defined as neural networks with at least two … level generation as an iterative
process that one needs to edit … is often a tall task, and is one of the core challenges of using …

… the relatively new paradigm of using machine learning (in contrast … inpainting, where a neural
network is trained to complete … 3) Back Propagation Artificial Neural Networks are universal …

… These models uses convolutional neural networks for … The image content creation has
been done using two inputs, … GlyphGAN utilizes L1 distance misfortune between the fake …

… of a mechanism of two neural networks contesting with its … , we could concatenate the
conditional information and data as … to discriminator consists of real images and fake images. We …

… and generation models are implemented using feed-forward … the corresponding real image
for a fake image. For the ith … that use GANs to train neural networks and augment the classical …

… well-designed neural network model, directly generating video … effectiveness of our gist
generation and conditional text filter, … a framework for generating video from text using a hybrid …

… In addition, we employ a regression neural network to position the items based on the provided
… utilized for the neural network model was acquired from Google's QuickDraw, an artificial …

… conditioned on expression categories have been proposed. … A synthetic data generation
system with a 3D convolutional … trained using the proposed approach’, we refer to networks …

… criterion using an attribute detection network combined with a … X2Face [41] uses one neural
network to resample the source … our network architecture rather then by the use of synthetic …