Awesome Domain Adaptation
A curated collection of papers, code, and resources for domain adaptation in machine learning.
What is Awesome Domain Adaptation?
Awesome Domain Adaptation is a curated GitHub repository that serves as a comprehensive index for research and resources in the field of domain adaptation. It systematically collects and categorizes academic papers, code implementations, and tutorials to help machine learning practitioners and researchers tackle the problem of model performance degradation when data distributions shift between source and target domains.
Machine learning researchers, PhD students, and engineers working on transfer learning, particularly those focused on making models robust to distribution shifts in applications like computer vision, autonomous driving, or medical AI.
It saves significant time in literature review by providing a single, continuously updated source for the state-of-the-art. The structured categorization by method and application area allows for efficient discovery of relevant techniques and reproducible code, which is often scattered across different platforms.
Overview
A collection of AWESOME things about domain adaptation
Use Cases
Best For
- Researchers conducting a literature review on domain adaptation methods
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