Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix a grammar error #70

Open
wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Fix a grammar error #70

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
17 changes: 11 additions & 6 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@ While research in Generative Adversarial Networks (GANs) continues to improve th
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 is a summary of some of the tricks.

[Here's a link to the authors of this document](#authors)

Expand All @@ -21,13 +21,16 @@ If we find it to be reasonable and verified, we will merge it in.
## 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)

- 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

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

## 3: Use a spherical Z

- Dont sample from a Uniform distribution

![cube](images/cube.png "Cube")
Expand All @@ -39,7 +42,6 @@ In practice, works well:
- 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](https://arxiv.org/abs/1609.04468) 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.
Expand All @@ -48,6 +50,7 @@ In practice, works well:
![batchmix](images/batchmix.png "BatchMix")

## 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
Expand Down Expand Up @@ -93,6 +96,7 @@ In practice, works well:
- If you do try it, have a principled approach to it, rather than intuition

For example

```
while lossD > A:
train D
Expand Down Expand Up @@ -128,12 +132,13 @@ while lossG > B:
- 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
Expand Down