# zhaoxin94/awesome-domain-adaptation

A collection of AWESOME things about domain adaptation

Repository: https://github.com/zhaoxin94/awesome-domain-adaptation
Canonical: https://ross.abutalabs.com/products/awesome-domain-adaptation
License: MIT
License Family: permissive
Topics: transfer-learning, domain-adaptation, adversarial-learning, image-translation, awesome-list, paper, zero-shot-learning, few-shot-learning, optimal-transport
Last push: 2025-12-08T06:04:32+00:00

## Health v2 (maintenance only)
Score: 57/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 56, release rhythm 35, longevity 100
- inputs: {"age_days": 3034, "days_push": 268, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5456, forks 881 (observed 2026-08-28T04:09:19.233460+00:00)

## What it is
A curated awesome-list collecting papers, code, libraries, tutorials, and benchmarks on domain adaptation and related transfer learning paradigms. It organizes research by method family (adversarial, self-training, optimal transport) and application area (segmentation, detection, medical imaging).

## Use cases
- find papers on unsupervised domain adaptation
- survey transfer learning research for a literature review
- find code implementations of domain adaptation methods
- research few-shot and zero-shot domain adaptation
- find benchmarks for domain adaptation experiments
- learn about sim-to-real transfer for robotics

## When to choose
- starting research on domain adaptation or transfer learning
- looking for a structured reading list of DA papers by category
- searching for open-source implementations of DA methods

## When to avoid
- you need a runnable library rather than a paper list
- you need general machine learning resources beyond transfer learning

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning
- domain: machine-learning, computer-vision, artificial-intelligence, awesome-lists
- platform: cross-platform
- tags: awesome-list, domain-adaptation, transfer-learning, papers, few-shot-learning, zero-shot-learning, optimal-transport

## Member repositories
- zhaoxin94/awesome-domain-adaptation (main) score 57

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:19.233460+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:56:48.139049+00:00, confidence not recorded.
  - readme: https://github.com/zhaoxin94/awesome-domain-adaptation (fetched 2026-08-28T04:09:19.233460+00:00, sha f32df01b4d78)
- Data as of 2026-08-30T08:39:29.467469+00:00.