# artix41/awesome-transfer-learning

Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)

Repository: https://github.com/artix41/awesome-transfer-learning
Canonical: https://ross.abutalabs.com/products/awesome-transfer-learning
License Family: other
Topics: transfer-learning, domain-adaptation, unsupervised-learning, paper, awesome-list
Last push: 2023-08-25T08:09:09+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3233, "days_push": 1104, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1776, forks 306 (observed 2026-08-28T04:05:34.589935+00:00)

## What it is
A curated awesome-list of papers, tutorials, datasets, and libraries on transfer learning and domain adaptation. It is no longer actively maintained but still accepts pull requests.

## Use cases
- find papers on unsupervised domain adaptation
- learn transfer learning fundamentals
- find datasets for domain adaptation experiments
- survey deep transfer learning methods
- find tutorials on fine-tuning and few-shot learning

## When to choose
- you need a starting point for research on transfer learning or domain adaptation
- you want a categorized reading list of papers and tutorials
- you are looking for datasets for domain-to-domain translation tasks

## When to avoid
- you need up-to-date resources, as the list is not actively maintained
- you need a software library or runnable code rather than references

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning
- domain: machine-learning, deep-learning, tutorials, awesome-lists
- platform: cross-platform
- tags: transfer-learning, domain-adaptation, awesome-list, papers, curated-list

## Member repositories
- artix41/awesome-transfer-learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:34.589935+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 2026-08-30T03:25:15.507104+00:00, confidence not recorded.
  - readme: https://github.com/artix41/awesome-transfer-learning (fetched 2026-08-28T04:05:34.589935+00:00, sha d55f7c4a4e85)
- Data as of 2026-08-30T08:39:29.467469+00:00.