Latent Optimisation is a technique used for generative adversarial networks to refine the sample quality of $z$. Specifically, it exploits knowledge from the discriminator $D$ to refine the latent source $z$. Intuitively, the gradient $\nabla\_{z}f\left(z\right) = \delta{f}\left(z\right)\delta{z}$ points in the direction that better satisfies the discriminator $D$, which implies better samples. Therefore, instead of using the randomly sampled $z \sim p\left(z\right)$, we uses the optimised latent: $$ \Delta{z} = \alpha\frac{\delta{f}\left(z\right)}{\delta{z}} $$ $$ z' = z + \Delta{z} $$ Source: LOGAN .
Euclidean Norm Regularization is a regularization step used in generative adversarial networks, and is typically added to both the generator and discriminator losses: $$ R\_{z} = w\_{r} \cdot ||\Delta{z}||^{2}\_{2} $$ where the scalar weight $w\_{r}$ is a parameter. Image: LOGAN
CS-GAN is a type of generative adversarial network that uses a form of deep compressed sensing, and latent optimisation, to improve the quality of generated samples.