2 repository-uri
Awesome GitHub RepositoriesMulti-Framework Model Collections
Libraries providing the same model implementations across multiple deep learning frameworks.
Distinct from TensorFlow Model Development: The candidates are focused on single-framework repositories (only PyTorch or only TensorFlow), whereas this is a multi-framework collection.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Multi-Framework Model Collections. Refine with filters or upvote what's useful.
Awesome Multi-Framework Model Collections GitHub Repositories
Găsește cele mai bune repo-uri cu AI.Vom căuta cele mai potrivite repository-uri folosind AI.
- wiseodd/generative-models
wiseodd/generative-models
7,497Vezi pe GitHubThis is a generative AI model library containing a collection of PyTorch and TensorFlow implementations for creating synthetic data and modeling complex probability distributions. It serves as a multi-framework repository of deep learning models designed for learning and replicating data patterns. The project provides specialized implementation suites for several generative architectures. This includes Generative Adversarial Networks using competing generator and discriminator models, Variational Autoencoder frameworks that map data to a latent space, and Restricted Boltzmann Machine and Deep
Offers a multi-framework collection of deep learning models implemented in both PyTorch and TensorFlow.
Python - trusted-ai/adversarial-robustness-toolbox
Trusted-AI/adversarial-robustness-toolbox
6,056Vezi pe GitHubThe Adversarial Robustness Toolbox (ART) is an open-source library that provides a unified framework for evaluating, defending, and certifying machine learning models against adversarial threats. It wraps models from any framework behind a common estimator interface, enabling composable pipelines for attack generation, defense application, robustness certification, and privacy auditing across evasion, poisoning, and extraction threats. The library distinguishes itself by covering the full adversarial ML security lifecycle within a single toolkit. It supports gradient-based adversarial example
Works with models built in TensorFlow, PyTorch, scikit-learn, and other popular frameworks without requiring code changes.
Pythonadversarial-attacksadversarial-examplesadversarial-machine-learning
