GAug VS TextAttack

Compare GAug vs TextAttack and see what are their differences.

GAug

AAAI'21: Data Augmentation for Graph Neural Networks (by zhao-tong)

TextAttack

TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/ (by QData)
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GAug TextAttack
1 3
181 2,754
- 1.3%
0.0 8.3
8 days ago about 1 month ago
Python Python
MIT License MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

GAug

Posts with mentions or reviews of GAug. We have used some of these posts to build our list of alternatives and similar projects.

TextAttack

Posts with mentions or reviews of TextAttack. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-07-06.

What are some alternatives?

When comparing GAug and TextAttack you can also consider the following projects:

TextFooler - A Model for Natural Language Attack on Text Classification and Inference

adversarial-robustness-toolbox - Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]

OpenAttack - An Open-Source Package for Textual Adversarial Attack.

auto-attack - Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"

spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python

KitanaQA - KitanaQA: Adversarial training and data augmentation for neural question-answering models

advertorch - A Toolbox for Adversarial Robustness Research

AIJack - Security and Privacy Risk Simulator for Machine Learning (arXiv:2312.17667)

alpha-beta-CROWN - alpha-beta-CROWN: An Efficient, Scalable and GPU Accelerated Neural Network Verifier (winner of VNN-COMP 2021, 2022, and 2023)

mlattacks - Machine Learning Attack Series

auto_LiRPA - auto_LiRPA: An Automatic Linear Relaxation based Perturbation Analysis Library for Neural Networks and General Computational Graphs