Latent Optimisation

Introduced by Wu et al. in Deep Compressed Sensing

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 .

Source: Deep Compressed Sensing

Latest Papers

PAPER DATE
LOGAN: Latent Optimisation for Generative Adversarial Networks
| Yan WuJeff DonahueDavid BalduzziKaren SimonyanTimothy Lillicrap
2019-12-02
Deep Compressed Sensing
| Yan WuMihaela RoscaTimothy Lillicrap
2019-05-16

Tasks

TASK PAPERS SHARE
Conditional Image Generation 1 33.33%
Image Generation 1 33.33%
Meta-Learning 1 33.33%

Components

COMPONENT TYPE
🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

Categories