View ic-loss on Snyk Open Source Advisor

Package Health Score

42 / 100

security
No known security issues
popularity
Limited
maintenance
Inactive
community
Limited

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frequency
39

Popularity

Limited

GitHub Stars: 0
Forks: 1
Contributors: 1

Direct Usage Popularity

Based on project statistics from the GitHub repository for the PyPI package ic-loss, we found that it has been starred ? times.

Security

No known security issues

Security and license risk for latest version

Release Date: May 13, 2021
Direct Vulnerabilities: 0
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0
H
0
M
0
L
Indirect Vulnerabilities: 0
C
0
H
0
M
0
L
License Risk: 0
H
0
M
0
L

All security vulnerabilities belong to production dependencies of direct and indirect packages.

License

MIT

Security Policy

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Maintenance

Inactive

Commit Frequency

No Recent Commits

Open Issues: 0
Open PR: 0
Last Release: 4 years ago
Last Commit: 3 years ago

Further analysis of the maintenance status of ic-loss based on released PyPI versions cadence, the repository activity, and other data points determined that its maintenance is Inactive.

An important project maintenance signal to consider for ic-loss is that it hasn't seen any new versions released to PyPI in the past 12 months, and could be considered as a discontinued project, or that which receives low attention from its maintainers.

In the past month we didn't find any pull request activity or change in issues status has been detected for the GitHub repository.

Community

Limited

Readme: Yes
Contributing.md: No
Code of Conduct: No
Contributors: 1
Funding: No

This project has seen only 10 or less contributors.

We found a way for you to contribute to the project! Looks like ic-loss is missing a Code of Conduct.

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Package

Python Versions Compatibility: ==3.*

Age: 4 years
Latest Release: 4 years ago
Dependencies: N/A
Versions: 2
Maintainers: 1
Wheels: OS Independent

Readme

Inverse Contrastive Loss

Implementations of inverse contrastive loss from Learning Invariant Representations using Inverse Contrastive Loss. The model architecture used on the ADNI dataset in the paper is also included along with PyTorch and Tensorflow implementations of the loss function.

Installation

$ pip install ic-loss

Usage

import torch
from ic_loss.losses import icl, icl_tf # icl - pytorch, icl_tf - tensorflow
from ic_loss.models import ADNIResNet # ADNIResNet - pytroch model used in the paper

model = ADNIResNet()

x = torch.randn([1, 1, 512, 512])
logits, latent = model(x)

loss = icl(latent, c) # c - extraneous attribute

FAQs

What is ic-loss?

Implementation of inverse contrastive loss. Visit Snyk Advisor to see a full health score report for ic-loss, including popularity, security, maintenance & community analysis.

Is ic-loss popular?

The python package ic-loss receives a total of 32 weekly downloads. As such, ic-loss popularity was classified as limited. Visit the popularity section on Snyk Advisor to see the full health analysis.

Is ic-loss well maintained?

We found indications that ic-loss is an Inactive project. See the full package health analysis to learn more about the package maintenance status.

Is ic-loss safe to use?

The python package ic-loss was scanned for known vulnerabilities and missing license, and no issues were found. Thus the package was deemed as safe to use. See the full health analysis review.

Last updated on 14 November-2024, at 12:02 (UTC).

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ic-loss

Implementation of inverse contrastive loss For more information about how to use this package see README

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Readme

Inverse Contrastive Loss

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Usage

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Readme

Inverse Contrastive Loss

Installation

Usage

FAQs

What is ic-loss?

Is ic-loss popular?

Is ic-loss well maintained?

Is ic-loss safe to use?

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Source Code

Open Source

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