Generative adversarial network (GAN) architecture. Adapted from https://github.com/PetarV-/TikZ/tree/master/Generative%20adversarial%20network.

A GAN has two parts. The discriminator $D$ acts as a classifier that learns to distinguish fake data produced by the generator $G$ from real data. $G$ incurs a penalty when $D$ detects implausible results. This signal is backpropagated through the generator weights such that $G$ learns to produce more realistic samples over time, eventually fooling the discriminator if training succeeds.

Edit and compile if you like:

% Generative adversarial network (GAN) architecture. % Adapted from https://github.com/PetarV-/TikZ/tree/master/Generative%20adversarial%20network. % A GAN has two parts. The discriminator $D$ acts as a classifier that learns to distinguish fake data produced by the generator $G$ from real data. $G$ incurs a penalty when $D$ detects implausible results. This signal is backpropagated through the generator weights such that $G$ learns to produce more realistic samples over time, eventually fooling the discriminator if training succeeds. \documentclass[tikz]{standalone} \usepackage{mathtools} \usetikzlibrary{calc,positioning} \begin{document} \begin{tikzpicture}[ ->, thick, node/.style={circle, fill=teal!60}, label/.style={below, font=\footnotesize}, ] \node[node] (zin) {$\vec z_\text{in}$}; \node[node, right=5em of zin] (fake) {$\vec x_\text{fake}$}; \draw (zin) -- node[above] {$G(\vec x)$} node[label] {generator} (fake); \draw[<-] (zin) -- node[above] {$p_\theta(\vec z)$} node[label] {latent noise} ++(-3,0); \node[node, above=of fake] (real) {$\vec x_\text{real}$}; \draw[<-] (real) -- node[above] {$p_\text{data}(\vec x)$} ++(-3,0); \node[node, right=6em of fake] (D) at ($(fake)!0.5!(real)$) {$\vec x$}; \node[right=7em of D] (out) {real?}; \draw (D) -- node[above] {$D(\vec x)$} node[label] {discriminator} (out); \coordinate[right=2.5em of fake, circle, fill, inner sep=0.15em] (pt1); \coordinate[right=2.5em of real, circle, fill, inner sep=0.15em] (pt2); \draw[-, dashed] (pt1) edge[bend left] coordinate[circle, fill=orange, inner sep=1mm, pos=0.7] (pt3) (pt2); \draw (fake) -- (pt1) (real) -- (pt2) (pt3) -- (D); \end{tikzpicture} \end{document}

Click to download: gan.tex

Open in Overleaf: gan.tex

This file is available on tikz.netlify.app and on GitHub and is MIT licensed.

See more on the author page of Janosh Riebesell..