# jindongwang/transferlearning

Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

Repository: https://github.com/jindongwang/transferlearning
Canonical: https://ross.abutalabs.com/products/transferlearning
Homepage: http://transferlearning.xyz/
Language: Python
License: MIT
License Family: permissive
Topics: transferlearning, domain-adaptation, transfer-learning, survey, deep-learning, generalization, few-shot, tutorial-code, theory, papers, few-shot-learning, meta-learning, domain-generalization, representation-learning, unsupervised-learning, machine-learning, self-supervised-learning, paper, style-transfer, domain-adaption
Last push: 2025-02-18T22:06:58+00:00

## Health v2 (maintenance only)
Score: 35/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 7, release rhythm 35, longevity 100
- inputs: {"age_days": 3412, "days_push": 561, "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 14348, forks 3838 (observed 2026-08-28T04:11:06.823472+00:00)

## What it is
A curated collection of papers, tutorials, code, datasets, and benchmarks covering transfer learning, domain adaptation, domain generalization, and related areas. It serves as a widely-cited reference hub for researchers and practitioners in machine learning.

## Use cases
- find papers on domain adaptation
- learn transfer learning from tutorials
- get benchmark datasets for domain generalization
- find code implementations of transfer learning methods
- prepare a survey or literature review on transfer learning
- find few-shot learning resources

## When to choose
- you need a comprehensive starting point for transfer learning research
- you want curated paper lists and tutorials on domain adaptation
- you need benchmark datasets and reference code

## When to avoid
- you need a production-ready transfer learning library with a stable API
- you need a maintained software tool rather than a resource collection

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, data-science
- domain: machine-learning, artificial-intelligence, tutorials, awesome-lists
- platform: python, cross-platform
- tags: transfer-learning, domain-adaptation, domain-generalization, few-shot-learning, meta-learning, papers, survey, benchmarks

## Member repositories
- jindongwang/transferlearning (main) score 35

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:06.823472+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 2026-08-29T17:12:38.636115+00:00, confidence not recorded.
  - readme: https://github.com/jindongwang/transferlearning (fetched 2026-08-28T04:11:06.823472+00:00, sha e8a623ba69c5)
  - homepage: http://transferlearning.xyz/ (fetched 2026-08-29T08:06:11.895709+00:00, sha bcfad2beefd4)
- Data as of 2026-08-30T08:39:29.467469+00:00.