alpha-beta-CROWN VS mn-bab

Compare alpha-beta-CROWN vs mn-bab and see what are their differences.

alpha-beta-CROWN

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

mn-bab

[ICLR 2022] Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Bound (by eth-sri)
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alpha-beta-CROWN mn-bab
1 1
206 7
3.4% -
4.7 1.8
about 1 month ago about 1 month ago
Python Python
GNU General Public License v3.0 or later -
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.

alpha-beta-CROWN

Posts with mentions or reviews of alpha-beta-CROWN. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-21.

mn-bab

Posts with mentions or reviews of mn-bab. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-10-21.

What are some alternatives?

When comparing alpha-beta-CROWN and mn-bab you can also consider the following projects:

TextAttack - TextAttack 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP https://textattack.readthedocs.io/en/master/

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

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

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