ambient-gan | reproduce results from the paper AmbientGAN

by AshishBora Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | ambient-gan Summary

ambient-gan is a Python library. ambient-gan has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

Code to reproduce results from the paper "AmbientGAN: Generative models from lossy measurements"

Support

Quality

Security

License

Reuse

Support

ambient-gan has a low active ecosystem.

It has 82 star(s) with 18 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 1 have been closed. On average issues are closed in 106 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ambient-gan is current.

Quality

ambient-gan has 0 bugs and 0 code smells.

Security

ambient-gan has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

ambient-gan code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ambient-gan is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

ambient-gan releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

ambient-gan saves you 1663 person hours of effort in developing the same functionality from scratch.

It has 3689 lines of code, 331 functions and 57 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed ambient-gan and discovered the below as its top functions. This is intended to give you an instant insight into ambient-gan implemented functionality, and help decide if they suit your requirements.

Infer from inputs
2d convolution layer
Create a weight variable
Creates a bias variable
Setup hparams
Get the path to the examples directory
Get mode name
Get task directory
Generate weights for a given tensor
Transpose x
Batch normalization
Measure the tensor
Get a list of filepaths
Sample theta from hparams
Unmeasure function
Calculate the blur of a numpy array
Measure the blur
Measure the image
Get dataframe from hparams and metrics
Constructs the discriminator layer
Get estimation metrics
Unconditional model
Conditional model function
A conditional model function
Unmeasure
Load MNIST dataset

Get all kandi verified functions for this library.

ambient-gan Key Features

No Key Features are available at this moment for ambient-gan.

ambient-gan Examples and Code Snippets

No Code Snippets are available at this moment for ambient-gan.

Community Discussions

No Community Discussions are available at this moment for ambient-gan.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install ambient-gan

You can download it from GitHub.
You can use ambient-gan 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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