This repository is a curated list of resources for transfer learning and domain adaptation, targeting researchers and practitioners in machine learning. It provides a comprehensive overview of papers, tutorials, datasets, and challenges, with a particular focus on domain adaptation techniques.

How It Works

The list categorizes resources by subfield, including unsupervised, semi-supervised, and few-shot domain adaptation, as well as applications in areas like computer vision and natural language processing. It highlights key papers and algorithms, often with links to their original publications and implementations, facilitating exploration of different adaptation strategies.

Quick Start & Requirements

This is a curated list, not a runnable library. No installation or specific requirements are needed to browse the content.

Highlighted Details

• Extensive categorization of papers by methodology (e.g., adversarial, optimal transport, subspace learning) and application domain.
• Includes a comprehensive list of datasets commonly used for domain adaptation benchmarks.
• Provides a table of results for various unsupervised domain adaptation methods on digit transfer tasks.
• Links to relevant books, tutorials, and challenges for deeper learning.

Maintenance & Community

The repository is not actively maintained but accepts pull requests for new contributions.

Licensing & Compatibility

The repository itself is not licensed as it is a list of links. The linked resources will have their own respective licenses.

Limitations & Caveats

The list is noted as not actively maintained, meaning newer resources may be missing. The results presented are from original papers and may not be directly comparable due to varying methodologies.