Home Glossary Wasserstein GAN (WGAN)

Wasserstein GAN (WGAN) - Page 10

A Wasserstein GAN, or WGAN, is a generative adversarial network designed to make training more stable by using a distance measure related to the Earth Mover distance. A generator creates samples while a critic scores how closely generated and real data distributions match. Unlike a traditional GAN discriminator, the critic does not simply classify samples as real or fake. The resulting training signal can remain useful even when the distributions barely overlap. WGAN variants are used for images, audio, and synthetic data, but they still require careful architecture choices, regularization, evaluation, and checks for bias, memorization, and unrealistic outputs.

Andrew Tulloch Leaves $12B AI Startup to Join Meta After Turning Down $1.5B Offer
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AI & Machine Learning, Immersive Reality (AR, VR, MR, and XR), News, Startups & Investment

Andrew Tulloch Leaves $12B AI Startup to Join Meta After Turning Down $1.5B Offer

By • 3 mins read

Andrew Tulloch, co-founder of the $12 billion AI startup Thinking Machines Lab, has joined Meta after previously rejecting what reports described as a $1.5 billion offer — a figure Meta has since called ‘inaccurate and ridiculous.’