# Brief Review — GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

**Proposes Two Time-scale Update Rule (TTUR)** and **Fréchet Inception Distance (FID)**

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash EquilibriumTTUR, Fréchet Inception Distance (FID), by Johannes Kepler University Linz2017 NIPS, Over 8500 Citations(Sik-Ho Tsang @ Medium)

Generative Adversarial Network (GAN)Image Synthesis:2014…2019[SAGAN]

==== My Other Paper Readings Are Also Over Here ====

**Two Time-scale Update Rule (TTUR)**is proposed for GAN training.**Fréchet Inception Distance (FID)**is also proposed as**a metric to****measure the generated quality**, which is better than Inception Score (IS).

# Outline

**Two Time-scale Update Rule (TTUR)****Fréchet Inception Distance (FID)****Results**

**1. Two Time-scale Update Rule (TTUR)**

TTUR has an

individual learning rateforboth the discriminator and the generator, which isbetter than using same learning ratesituations as above.

- And
**GAN****s can converge to a local Nash equilibrium when trained by a TTUR**, i.e., when discriminator and generator have separate learning rates. - For a two time-scale update rule (TTUR),
**the learning rates**, respectively:*b*(*n*) and*a*(*n*) are used for the discriminator and the generator update

**2. Fréchet Inception Distance (FID)**

“Fréchet Inception Distance” (FID)isthe Fréchet distancethe , which is given by:d(., .) between the Gaussian with mean (m,C) obtained fromp(.) and the Gaussian with mean (mw,Cw) obtained frompw(.)

- For computing the FID,
**all images are propagated from the training dataset**through the pretrained Inception-v3 model following the computation of the Inception Score. However, the last pooling layer is used as coding layer. For this coding layer, the**mean**and the*mw***covariance matrix**are calculated.*Cw* - To approximate the moments for the model distribution,
**50,000 images are generated**, propagated through the Inception-v3 model, and then the**mean**and the*m***covariance matrix**are computed.*C*

- Noises with different noise levels and different types are added to CelebA images as above.

Fréchet Inception Distance (FID) captures the disturbance level very well.FID ismore consistent with the noise levelthan the Inception Score.

# 3. Results

**FID**is used for**image**evaluation.**Jensen-Shannon-divergence (JSD)**is used for**language**benchmark.

Using

TTURobtainsbetterperformance than the original training strategy.

The best FID is obtained with TTUR. TTUR constantly outperforms standard training and ismore stable.

Again

TTUR reaches lower FIDsthan one time-scale training.

The improvement of TTUR on the 6-gram statistics over original training shows that

TTUR enables to learn to generate more subtle pseudo-words which better resembles real words.