Andrew Brock, first author of the high-profile research paper Large Scale GAN Training for High Fidelity Natural Image Synthesis (aka “BigGAN”), has posted a GitHub repository of an unofficial PyTorch BigGAN implementation that requires only 4-8 GPUs to train the model.
GTC 2019 runs next Monday through Thursday (March 18 — 21), and while we can only speculate what surprises NVIDIA CEO Jensen Huang might have in store for us, we can get some sense of where the company is headed by looking at what it’s been up to for the last 12 months.
As Synced previously reported, these hyperrealistic images now flooding the Internet come from US chip giant NVIDIA’s StyleGAN, a generative adversarial network based face generator that performs so well that most people can’t distinguish its creations from photos of real people.
In December Synced reported on a hyperrealistic face generator developed by US chip giant NVIDIA. The GAN-based model performs so well that most people can’t distinguish the faces it generates from real photos. This week NVIDIA announced that it is open-sourcing the nifty tool, which it has dubbed “StyleGAN”.
The number of AI-related research papers has skyrocketed in recent years, outpacing papers from all other academic topics since 2000. This has, not unsurprisingly, resulted in a shortage of qualified peer reviewers in the machine learning community, particularly when it comes to conference paper submissions.
Text-based CAPTCHA remain one of the most visible and commonly used mechanisms for website security. As a sort of online gatekeeper that distinguishes between humans and bots, the little solvable image fields have critical commercial applications in blocking automatic spam and preventing e-transfer fraud; and can also stop bots from spreading fraudulent information, etc.
The digital painting tool GANpaint has gone viral on social media. The product of a team of high-profile researchers from MIT, IBM, Google, and the Chinese University of Hong Kong, GAPpaint allows anyone — even those with little knowledge of digital painting or photoshop — to “paint” incredibly complex and detailed photorealistic scenes.
Founded in 1999, Tokyo-based DeNA has developed popular platforms and services for gaming, E-commerce, automotive, healthcare and entertainment content distribution. As AI continues transforming all things digital, DeNA is expanding its deep learning tech capabilities to support R&D on new techniques.
Georgia Tech and Google Brain researchers have introduced the new interactive tool GAN Lab, which visually presents the training process of complex machine learning model Generative Adversarial Networks (GANs). Even machine learning newbs can now experiment with GAN models using only a common web browser.
In a new paper Durham University researchers introduce a anomaly detection model, GANomaly, comprising a conditional generative adversarial network that “jointly learns the generation of high-dimensional image space and the inference of latent space.” The process enables the model to perform anomaly detection tasks even in sample-poor environments.
Electrifying an entire dance club is easy if you have killer moves like John Travolta in Saturday Night Fever. But for the rest of us, not so much. We may shake our butts and swing our arms, but let’s face it: some people just can’t dance. But now there’s hope, thanks to AI.
CVPR 2017 conference covered topics in: Machine Learning, Object Recognition & Scene Understanding – Computer Vision & Language, 3D Vision, Human Analyzing, Low- & Mid- Level Vision, Image Motion & Tracking: Video Analysis, Computational Photography, Applications.
PixelGAN is an autoencoder for which the generative path is a convolutional autoregressive neural network on pixels, conditioned on a latent code, and the recognition path uses a generative adversarial network (GAN) to impose a prior distribution on the latent code.