- Researchers from NVIDIA recently published a paper detailing their new methodology for generative adversarial networks (GANs) that generated photorealistic pictures of fake celebrities.
- Rather than train a single neural network to recognize pictures, researchers train two competing networks.
- “The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses,” explained the researchers in their paper Progressive Growing of GANs for Improved Quality, Stability and Variation.
- Since the publicly available CelebFaces Attributes (CelebA) training dataset varied in resolution and visual quality — and not sufficient enough for high output resolution — the researchers generated a higher-quality version of the dataset consisting of 30,000 images at 1024 x 1024 resolution.
- Generating convincing realistic images with GANs are within reach and the researchers plan to use TensorFlow and multi-GPUs for the next part of the work.
Researchers from NVIDIA recently published a paper detailing their new methodology for generative adversarial networks (GANs) that generated photorealistic pictures of fake celebrities.
Continue reading “Generating Photorealistic Images of Fake Celebrities with Artificial Intelligence – NVIDIA Developer News Center”
- Scripts to install and setup Tensorflow and it’s dependencies on Ubuntu.
- to install everything in one command.
- Scripts are included to install Java, Bazel, CUDA, Tensorflow and Docker.
- Scripts can be used inside a docker container to install everything in one command or one at a time.
Tensorflow-setup-scripts – Scripts to install and setup Tensorflow and it’s dependencies on Ubuntu
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- Developers from Orange Labs in France developed a deep learning system that can quickly make young faces look older, and older faces look younger.
- Using CUDA, Tesla K40 GPUs and cuDNN for the deep learning work, they trained their neural network on 5,000 faces from each age group (0-18, 19- 29, 30-39, 40-49, 50-59, and 60+ years old) taken from the Internet Movie Database and from Wikipedia and then labeled with the person’s age — this helped the system learn the characteristic signature of faces in each age group.
- A second neural network, called the face discriminator, looks at the synthetically aged face to see whether the original identity can still be picked out.
- If it can’t, the image is rejected, which they call the process in their paper, Age Conditional Generative Adversarial Network.
- Grigory Antipov of Orange Labs mentioned the technique could be used in applications such as helping identify people who have been missing for many years.
Developers from Orange Labs in France developed a deep learning system that can quickly make young faces look older, and older faces look younger. A number of techniques already exist, but they are expensive and time consuming.
Continue reading “Create Realistic Synthetic Faces That Look Older With Deep Learning – News Center”
- Large cleanup to add second order gradient for ops with C++ gradients and improve existing gradients such that most ops can now be differentiated multiple times.
- TensorFlow now builds and runs on Microsoft Windows (tested on Windows 10, Windows 7, and Windows Server 2016).
- Improve trace, matrix_set_diag , matrix_diag_part and their gradients to work for rectangular matrices.
- Added a new library for library of matrix-free (iterative) solvers for linear equations, linear least-squares, eigenvalues and singular values in tensorflow/contrib/solvers.
- C API: Type TF_SessionWithGraph has been renamed to TF_Session , indicating its preferred use in language bindings for TensorFlow.
tensorflow – Computation using data flow graphs for scalable machine learning
Continue reading “Release TensorFlow v0.12.0 RC0 · tensorflow/tensorflow · GitHub”
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- TensorLayer is a deep learning and reinforcement learning library for researchers and practitioners.
TensorLayer is a deep learning and reinforcement learning library for researchers and practitioners. It is an extension library for Google TensorFlow. It providers high-level APIs and pre-built training blocks that can largely simplify the development of complex learning models. TensorLayer is easy to be extended and customized for your needs. In addition, we provide a rich set of examples and tutorials to help you to build up your own deep learning and reinforcement learning algorithms.
Continue reading “Welcome to TensorLayer — TensorLayer 1.1 documentation”
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Visual computing technology from NVIDIA: inventor of the GPU, which creates interactive graphics on laptops, workstations, mobile devices, notebooks, PCs, and more.
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