bwgan | Code for the paper Banach Wasserstein GAN
kandi X-RAY | bwgan Summary
kandi X-RAY | bwgan Summary
bwgan is a Python library. bwgan has no bugs, it has no vulnerabilities and it has low support. However bwgan build file is not available. You can download it from GitHub.
Traditional WGAN uses an approximation of the Wasserstein metric to opimize the generator. This Wasserstein metric in turn depends upon an underlying metric on images which is taken to be the norm. The article extends the theory of WGAN-GP to any Banach space, while this code can be used to train WGAN over any Sobolev space with norm. The parameters p can be used to control the focus on outliers, with high p indicating a strong focus on the worst offenders. s can be used to control focus on small/large scale behaviour, where negative s indicates focus on large scales, while positive s indicates focus on small scales (e.g. edges).
Traditional WGAN uses an approximation of the Wasserstein metric to opimize the generator. This Wasserstein metric in turn depends upon an underlying metric on images which is taken to be the norm. The article extends the theory of WGAN-GP to any Banach space, while this code can be used to train WGAN over any Sobolev space with norm. The parameters p can be used to control the focus on outliers, with high p indicating a strong focus on the worst offenders. s can be used to control focus on small/large scale behaviour, where negative s indicates focus on large scales, while positive s indicates focus on small scales (e.g. edges).
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Quality
Security
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Support
bwgan has a low active ecosystem.
It has 28 star(s) with 8 fork(s). There are 6 watchers for this library.
It had no major release in the last 6 months.
There are 0 open issues and 1 have been closed. On average issues are closed in 4 days. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of bwgan is current.
Quality
bwgan has 0 bugs and 0 code smells.
Security
bwgan has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
bwgan code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
bwgan does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
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bwgan releases are not available. You will need to build from source code and install.
bwgan has no build file. You will be need to create the build yourself to build the component from source.
Installation instructions are not available. Examples and code snippets are available.
Top functions reviewed by kandi - BETA
kandi has reviewed bwgan and discovered the below as its top functions. This is intended to give you an instant insight into bwgan implemented functionality, and help decide if they suit your requirements.
- Computes discriminator
- Resblock convolutional block
- Resblock convolutional
- Applies a convolution layer
- Downsamples x
- R Activu activation
- Downsampling function
- Multiply a mean pool of x
- Generate tensorflow
- Batch norm
- Upsample x
- Upsamples x
- Apply a smoothing filter
- A stable norm
Get all kandi verified functions for this library.
bwgan Key Features
No Key Features are available at this moment for bwgan.
bwgan Examples and Code Snippets
No Code Snippets are available at this moment for bwgan.
Community Discussions
No Community Discussions are available at this moment for bwgan.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install bwgan
You can download it from GitHub.
You can use bwgan like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use bwgan like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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