• Sample m noise samples 1, 2, … , from a distribution • Obtaining generated data 1, 2, … , , = • Update discriminator parameters to maximize • ෨ = 1 σ =1 + 1 σ =1 1 − • ← + ෨ • Sample m noise samples 1, 2, … , from a distribution • Update generator parameters to minimize • ෨ = 1 σ =1 1 − • ← − ෨ Learning D Learning G Training Algorithm of GANs Repeat k times