# yassouali/awesome-semi-supervised-learning

😎 An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.

Repository: https://github.com/yassouali/awesome-semi-supervised-learning
Canonical: https://ross.abutalabs.com/products/awesome-semi-supervised-learning
License: MIT
License Family: permissive
Topics: deep-learning, semi-supervised-learning, machine-learning, computer-vision, natural-language-processing, graph-neural-networks, generative-model
Last push: 2024-05-24T02:46:31+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2339, "days_push": 831, "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 1856, forks 224 (observed 2026-08-28T04:05:44.917443+00:00)

## What it is
A curated, regularly updated list of semi-supervised learning papers, methods, and resources, covering deep learning, computer vision, NLP, graph neural networks, and generative models. It serves as a reference catalog for researchers and practitioners rather than executable software.

## Use cases
- find recent semi-supervised learning papers
- survey methods for training with limited labeled data
- research consistency regularization and self-training techniques
- find semi-supervised learning resources for computer vision
- explore semi-supervised approaches for NLP and graph neural networks
- prepare a literature review on semi-supervised learning

## When to choose
- you need an up-to-date curated bibliography of semi-supervised learning research
- you are starting research on learning from limited labeled data
- you want organized pointers to methods across vision, NLP, and graphs

## When to avoid
- you need runnable code or a library rather than a paper list
- you need supervised or fully self-supervised learning resources
- you need a tutorial with hands-on exercises instead of links

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, nlp, computer-vision
- domain: machine-learning, deep-learning, computer-vision, awesome-lists, tutorials
- platform: cross-platform
- tags: awesome-list, semi-supervised-learning, papers, curated-resources, research, natural-language-processing

## Member repositories
- yassouali/awesome-semi-supervised-learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:44.917443+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 2026-08-30T03:16:42.773751+00:00, confidence not recorded.
  - readme: https://github.com/yassouali/awesome-semi-supervised-learning (fetched 2026-08-28T04:05:44.917443+00:00, sha c9a62415e651)
- Data as of 2026-08-30T08:39:29.467469+00:00.