Generative adversarial networks (GANs) are deep neural net architectures comprised of two nets, pitting one against the other (thus the “adversarial”).
GANs were introduced in a paper by Ian Goodfellow and other researchers at the University of Montreal, including Yoshua Bengio, in 2014. Referring to GANs, Facebook’s AI research director Yann LeCun called adversarial training “the most interesting idea in the last 10 years in ML.”
GANs’ potential is huge, because they can learn to mimic any distribution of data. That is, GANs can be taught to create worlds eerily similar to our own in any domain: images, music, speech, prose. They are robot artists in a sense, and their output is impressive – poignant even. Read More
Daily Archives: December 16, 2019
Going Beyond GAN? New DeepMind VAE Model Generates High Fidelity Human
Generative adversarial networks (GANs) have become AI researchers’ “go-to” technique for generating photo-realistic synthetic images. Now, DeepMind researchers say that there may be a better option.
In a new paper, the Google-owned research company introduces its VQ-VAE 2 model for large scale image generation. The model is said to yield results competitive with state-of-the-art generative model BigGAN in synthesizing high-resolution images while delivering broader diversity and overcoming some native shortcomings of GANs. Read More
The Worldwide Web of Chinese and Russian Information Controls
The global diffusion of Chinese and Russian information control technology and techniques has featured prominently in the headlines of major international newspapers.1 Few stories, however, have provided a systematic analysis of both the drivers and outcomes of such diffusion. This paper does so – and finds that these information controls are spreading more efficiently to countries with hybrid or authoritarian regimes, particularly those that have ties to China or Russia. Chinese information controls spread more easily to countries along the Belt and Road Initiative; Russian controls spread to countries within the Commonwealth of Independent States. In arriving at these findings, this working paper first defines the Russian and Chinese models of information control and then traces their diffusion to the 110 countries within the countries’ respective technological spheres, which are geographical areas and spheres of influence to which Russian and Chinese information control technology, techniques of handling information, and law have diffused. Read More
#china, #russia, #surveillance
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