docs: update changelog

CHANGELOG.md

@@ -6,9 +6,16 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 ### In Progress
-- [MS-SSIM loss training option](https://github.com/faceshiftlabs/DeepFaceLab/tree/feature/ms-ssim-loss-2)
 - [Freezeable layers (encoder/decoder/etc.)](https://github.com/faceshiftlabs/DeepFaceLab/tree/feature/freezable-weights)
-- [GAN stability improvements](https://github.com/faceshiftlabs/DeepFaceLab/tree/feature/gan-updates)
+
+## [1.4.0] - 2020-03-24
+### Added
+- [MS-SSIM loss training option](doc/features/ms-ssim)
+- GAN version option (v2 - late 2020 or v3 - current GAN)
+- [GAN label smoothing and label noise options](doc/features/gan-options)
+### Fixed
+- Background Power now uses the entire image, not just the area outside of the mask for comparison.
+This should help with rough areas directly next to the mask
 
 ## [1.3.0] - 2020-03-20
 ### Added
@@ -53,7 +60,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Reset stale master branch to [seranus/DeepFaceLab](https://github.com/seranus/DeepFaceLab), 
   21 commits ahead of [iperov/DeepFaceLab](https://github.com/iperov/DeepFaceLab) ([compare](https://github.com/iperov/DeepFaceLab/compare/4818183...seranus:3f5ae05))
 
-[Unreleased]: https://github.com/olivierlacan/keep-a-changelog/compare/v1.3.0...HEAD
+[Unreleased]: https://github.com/olivierlacan/keep-a-changelog/compare/v1.4.0...HEAD
+[1.4.0]: https://github.com/faceshiftlabs/DeepFaceLab/compare/v1.3.0...v1.4.0
 [1.3.0]: https://github.com/faceshiftlabs/DeepFaceLab/compare/v1.2.1...v1.3.0
 [1.2.1]: https://github.com/faceshiftlabs/DeepFaceLab/compare/v1.2.0...v1.2.1
 [1.2.0]: https://github.com/faceshiftlabs/DeepFaceLab/compare/v1.1.5...v1.2.0

doc/features/gan-options/README.md

@@ -0,0 +1,50 @@
+# GAN Options
+
+Allows you to use one-sided label smoothing and noisy labels when training the discriminator.
+
+- [ONE-SIDED LABEL SMOOTHING](#one-sided-label-smoothing)
+- [USAGE](#usage)
+
+## ONE-SIDED LABEL SMOOTHING
+
+![](tutorial-on-theory-and-application-of-generative-adversarial-networks-54-638.jpg)
+
+> Deep networks may suffer from overconfidence. For example, it uses very few features to classify an object. To 
+> mitigate the problem, deep learning uses regulation and dropout to avoid overconfidence. 
+> 
+> In GAN, if the discriminator depends on a small set of features to detect real images, the generator may just produce 
+> these features only to exploit the discriminator. The optimization may turn too greedy and produces no long term 
+> benefit. In GAN, overconfidence hurts badly. To avoid the problem, we penalize the discriminator when the prediction 
+> for any real images go beyond 0.9 (D(real image)>0.9). This is done by setting our target label value to be 0.9 
+> instead of 1.0.
+ - [GAN — Ways to improve GAN performance](https://towardsdatascience.com/gan-ways-to-improve-gan-performance-acf37f9f59b)
+
+By setting the label smoothing value to any value > 0, the target label value used with the discriminator will be:
+```
+target label value = 1 - (label smoothing value)
+```
+### USAGE
+
+```
+[0.1] GAN label smoothing ( 0 - 0.5 ?:help ) : 0.1
+```
+
+## Noisy labels
+
+> make the labels the noisy for the discriminator: occasionally flip the labels when training the discriminator
+ - [How to Train a GAN? Tips and tricks to make GANs work](https://github.com/soumith/ganhacks/blob/master/README.md#6-use-soft-and-noisy-labels)
+
+By setting the noisy labels value to any value > 0, then the target labels used with the discriminator will be flipped
+("fake" => "real" / "real" => "fake") with probability p (where p is the noisy label value).
+
+E.g., if the value is 0.05, then ~5% of the labels will be flipped when training the discriminator
+
+### USAGE
+```
+[0.05] GAN noisy labels ( 0 - 0.5 ?:help ) : 0.05
+```
+
+
+
+
+

doc/features/ms-ssim/README.md

@@ -3,11 +3,12 @@
 Allows you to train using the MS-SSIM (multiscale structural similarity index measure) as the main loss metric,
 a perceptually more accurate measure of image quality than MSE (mean squared error).
 
+As an added benefit, you may see a decrease in ms/iteration (when using the same batch size) with Multiscale loss
+enabled. You may also be able to train with a larger batch size with it enabled.
+
 - [DESCRIPTION](#description)
 - [USAGE](#usage)
 
-![](example.png)
-
 ## DESCRIPTION
 
 [SSIM](https://en.wikipedia.org/wiki/Structural_similarity) is metric for comparing the perceptial quality of an image: