This repository serves as a comprehensive, curated collection of resources for transfer learning, domain adaptation, and domain generalization. It targets researchers, engineers, and students interested in these machine learning subfields, offering a centralized hub for papers, code, datasets, tutorials, and theoretical foundations.

How It Works

The project acts as a meta-repository, aggregating and organizing a vast array of academic and practical resources related to transfer learning. It categorizes information by research area (e.g., domain adaptation, few-shot learning, multi-task learning), theoretical concepts, and practical applications, providing links to papers, code repositories, datasets, and tutorials. This structured approach facilitates efficient discovery and learning within the complex landscape of transfer learning.

Quick Start & Requirements

• Code Execution: Many code examples can be run directly in GitHub Codespaces or via github.dev without local setup.
• Dependencies: Specific code examples will have their own Python dependencies, often including PyTorch.
• Resources: Links to Google Colab notebooks are provided for instant execution of certain examples.
• Official Resources:
  • Website: transferlearning.xyz
  • Papers: doc/awesome_papers.md
  • Datasets & Benchmarks: HERE

Highlighted Details

• Extensive collection of survey papers covering various aspects of transfer learning from 2015 to 2023.
• Links to unified codebases for deep domain adaptation and deep domain generalization.
• Curated lists of prominent scholars, labs, and thesis related to transfer learning.
• Includes resources for specific sub-areas like knowledge distillation, negative transfer, and federated transfer learning.

Maintenance & Community

The repository is actively updated, with recent paper additions noted in February 2024. It encourages community contributions for expanding the lists of scholars, papers, and resources.

Licensing & Compatibility

The repository itself appears to be under a permissive license, but it contains links to PDFs and thesis that are explicitly stated to be for academic purposes only, with copyrights belonging to their respective publishers or organizations. Commercial use of these specific linked materials may be restricted.

Limitations & Caveats

The repository is a curated list and not a unified framework; users must manage individual code dependencies. Some linked PDF/thesis materials are restricted to academic use, potentially limiting commercial application. The lists of scholars and papers are noted as incomplete.