imsat | Reproducing code for the paper Learning Discrete | Machine Learning library

by weihua916 Python Version: Current License: No License

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kandi X-RAY | imsat Summary

imsat is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. imsat has no bugs, it has no vulnerabilities and it has low support. However imsat build file is not available. You can download it from GitHub.

This is a reproducing code for IMSAT [1]. IMSAT is a method for discrete representation learning using deep neural networks. It can be applied to clustering and hash learning to achieve the state-of-the-art results. This is the work performed while Weihua Hu was interning at Preferred Networks.

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Quality

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Support

imsat has a low active ecosystem.

It has 72 star(s) with 19 fork(s). There are 6 watchers for this library.

It had no major release in the last 6 months.

There are 2 open issues and 0 have been closed. On average issues are closed in 535 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of imsat is current.

Quality

imsat has 0 bugs and 0 code smells.

Security

imsat has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

imsat code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

imsat 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.

Reuse

imsat releases are not available. You will need to build from source code and install.

imsat has no build file. You will be need to create the build yourself to build the component from source.

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

It has 402 lines of code, 25 functions and 4 files.

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

Top functions reviewed by kandi - BETA

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

Calculate loss information for loss function .
Compute the loss of the loss function .
Random variates .
Load MNIST dataset .
Initialize layer .
Batch normalization .
Calculate the entropy of a matrix .
Get n values from GPU .
Compute the loss of the loss .
compute the distance between two points

Get all kandi verified functions for this library.

imsat Key Features

No Key Features are available at this moment for imsat.

imsat Examples and Code Snippets

No Code Snippets are available at this moment for imsat.

Community Discussions

Trending Discussions on imsat

How to desaturate an image using Emgu c#

QUESTION

How to desaturate an image using Emgu c#

Asked 2019-Feb-12 at 02:03

I know that we desaturate an image by decreasing the values in the Saturation channel. I want to acomplish this using c# with emgu

For instance here is c++ code using opencv to do so:

...

ANSWER

Answered 2019-Feb-12 at 02:03

I put a VectorOfMat into the CvInvoke.Merge and it works.

Source https://stackoverflow.com/questions/54641456

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

Vulnerabilities

No vulnerabilities reported

Install imsat

For reproducing the experiments on MNIST datasets in [1], run the following codes. calculate_distance.py can be used to calculate the perturbation range for Virtual Adversarial Training [2]. For MNIST dataset, we have already calculated the range.
Clustering with MNIST: python imsat_cluster.py
Hash learning with MNIST: python imsat_hash.py

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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