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A curated list of awesome adversarial machine learning resources
The main features of yenchenlin/awesome-adversarial-machine-learning are: Awesome List, Curated Research Lists, Simulation Environments, Awesome Lists.
Projects with overlapping indexed features include: awesomedata/awesome-public-datasets — This project is a community-maintained, open-access directory of high-quality public datasets. It serves as a… jphall663/awesome-machine-learning-interpretability — A curated list of awesome responsible machine learning resources. dk-liang/awesome-visual-transformer — Collect some papers about transformer with vision. Awesome Transformer with Computer Vision (CV). christoschristofidis/awesome-deep-learning — This project is a curated directory of resources, libraries, and frameworks designed to support the development,… josephmisiti/awesome-machine-learning — This project is a comprehensive, community-driven directory of machine learning resources, software libraries, and… kjw0612/awesome-deep-vision — A curated list of deep learning resources for computer vision.
This project is a community-maintained, open-access directory of high-quality public datasets. It serves as a centralized reference point for researchers, developers, and data scientists to locate reliable information sources across a wide spectrum of industries and scientific fields. By providing a structured index, the repository facilitates the discovery of data necessary for exploratory analysis, machine learning model training, and the development of data-intensive applications. The directory distinguishes itself through a lightweight, platform-agnostic approach to resource indexing that
Collect some papers about transformer with vision. Awesome Transformer with Computer Vision (CV)
This project is a curated directory of resources, libraries, and frameworks designed to support the development, training, and deployment of neural network models. It serves as a comprehensive guide for navigating the machine learning ecosystem, providing structured access to software utilities and research materials. The directory distinguishes itself by aggregating tools across the entire machine learning lifecycle, ranging from data management and experiment tracking to production-ready model deployment. It functions as a central hub for discovering both foundational academic research and
This project is a comprehensive, community-driven directory of machine learning resources, software libraries, and educational materials. It serves as a centralized knowledge base for developers and researchers, organizing tools and frameworks by their primary programming language and technical domain to simplify discovery across the artificial intelligence ecosystem. The collection distinguishes itself by providing a cross-language development index that spans diverse programming environments, including C, C++, Rust, Clojure, and Python. It covers a wide range of specialized capabilities, fr