(this list is no longer maintained, and I am not sure how relevant it is in 2020)

How to Train a GAN? Tips and tricks to make GANs work

While research in Generative Adversarial Networks (GANs) continues to improve the fundamental stability of these models, we use a bunch of tricks to train them and make them stable day to day.

Here are a summary of some of the tricks.

Here's a link to the authors of this document

If you find a trick that is particularly useful in practice, please open a Pull Request to add it to the document. If we find it to be reasonable and verified, we will merge it in.

1. Normalize the inputs

normalize the images between -1 and 1
Tanh as the last layer of the generator output

2: A modified loss function

In GAN papers, the loss function to optimize G is min (log 1-D), but in practice folks practically use max log D

because the first formulation has vanishing gradients early on
Goodfellow et. al (2014)

In practice, works well:

Flip labels when training generator: real = fake, fake = real

3: Use a spherical Z

Dont sample from a Uniform distribution

Sample from a gaussian distribution

When doing interpolations, do the interpolation via a great circle, rather than a straight line from point A to point B
Tom White's Sampling Generative Networks ref code https://github.com/dribnet/plat has more details

4: BatchNorm

Construct different mini-batches for real and fake, i.e. each mini-batch needs to contain only all real images or all generated images.
when batchnorm is not an option use instance normalization (for each sample, subtract mean and divide by standard deviation).

5: Avoid Sparse Gradients: ReLU, MaxPool

the stability of the GAN game suffers if you have sparse gradients
LeakyReLU = good (in both G and D)
For Downsampling, use: Average Pooling, Conv2d + stride
For Upsampling, use: PixelShuffle, ConvTranspose2d + stride
- PixelShuffle: https://arxiv.org/abs/1609.05158

6: Use Soft and Noisy Labels

Label Smoothing, i.e. if you have two target labels: Real=1 and Fake=0, then for each incoming sample, if it is real, then replace the label with a random number between 0.7 and 1.2, and if it is a fake sample, replace it with 0.0 and 0.3 (for example).
- Salimans et. al. 2016
make the labels the noisy for the discriminator: occasionally flip the labels when training the discriminator

7: DCGAN / Hybrid Models

Use DCGAN when you can. It works!
if you cant use DCGANs and no model is stable, use a hybrid model : KL + GAN or VAE + GAN

8: Use stability tricks from RL

Experience Replay
- Keep a replay buffer of past generations and occassionally show them
- Keep checkpoints from the past of G and D and occassionaly swap them out for a few iterations
All stability tricks that work for deep deterministic policy gradients
See Pfau & Vinyals (2016)

9: Use the ADAM Optimizer

optim.Adam rules!
- See Radford et. al. 2015
Use SGD for discriminator and ADAM for generator

10: Track failures early

D loss goes to 0: failure mode
check norms of gradients: if they are over 100 things are screwing up
when things are working, D loss has low variance and goes down over time vs having huge variance and spiking
if loss of generator steadily decreases, then it's fooling D with garbage (says martin)

11: Dont balance loss via statistics (unless you have a good reason to)

Dont try to find a (number of G / number of D) schedule to uncollapse training
It's hard and we've all tried it.
If you do try it, have a principled approach to it, rather than intuition

For example

while lossD > A:
  train D
while lossG > B:
  train G

12: If you have labels, use them

if you have labels available, training the discriminator to also classify the samples: auxillary GANs

13: Add noise to inputs, decay over time

Add some artificial noise to inputs to D (Arjovsky et. al., Huszar, 2016)
- http://www.inference.vc/instance-noise-a-trick-for-stabilising-gan-training/
- https://openreview.net/forum?id=Hk4_qw5xe
adding gaussian noise to every layer of generator (Zhao et. al. EBGAN)
- Improved GANs: OpenAI code also has it (commented out)

14: [notsure] Train discriminator more (sometimes)

especially when you have noise
hard to find a schedule of number of D iterations vs G iterations

15: [notsure] Batch Discrimination

Mixed results

16: Discrete variables in Conditional GANs

Use an Embedding layer
Add as additional channels to images
Keep embedding dimensionality low and upsample to match image channel size

17: Use Dropouts in G in both train and test phase

Provide noise in the form of dropout (50%).
Apply on several layers of our generator at both training and test time
https://arxiv.org/pdf/1611.07004v1.pdf

Authors

Soumith Chintala
Emily Denton
Martin Arjovsky
Michael Mathieu

muyuguo/ganhacks