N-gated Hacker News🎓 Ah yes, another 8-minute math lecture turned #snoozefest where "Generative Adversarial Networks" are explained like a bedtime story for data science insomniacs. 🤖💤 Spoiler: it's just another epic battle between a generator and a discriminator, but don't hold your breath for any worthwhile entertainment or clarity. 🥱
https://jaketae.github.io/study/gan-math/ #GenerativeAdversarialNetworks #MathLecture #DataScience #AIEntertainment #HackerNews #ngated
https://jaketae.github.io/study/gan-math/ #GenerativeAdversarialNetworks #MathLecture #DataScience #AIEntertainment #HackerNews #ngated
The Math Behind GANs
Generative Adversarial Networks refer to a family of generative models that seek to discover the underlying distribution behind a certain data generating process. This distribution is discovered through an adversarial competition between a generator and a discriminator. As we saw in an earlier introductory post on GANs, the two models are trained such that the discriminator strives to distinguish between generated and true examples, while the generator seeks to confuse the discriminator by producing data that are as realistic and compelling as possible.
Refurio Anachro