# asheeshcric/awesome-contrastive-self-supervised-learning

A comprehensive list of awesome contrastive self-supervised learning papers.

Repository: https://github.com/asheeshcric/awesome-contrastive-self-supervised-learning
Canonical: https://ross.abutalabs.com/products/awesome-contrastive-self-supervised-learning
License Family: other
Topics: hacktoberfest-accepted, self-supervised-learning, representation-learning, unsupervised-learning, deep-learning, transfer-learning
Last push: 2024-09-10T05:04:32+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": 2275, "days_push": 722, "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 1309, forks 123 (observed 2026-08-28T04:04:19.418579+00:00)

## What it is
A curated awesome-list of research papers on contrastive self-supervised learning, organized by year and topic with links to arXiv papers and code repositories. It serves as a reading guide for researchers and practitioners in representation learning.

## Use cases
- find papers on contrastive self-supervised learning
- research representation learning methods like SimCLR and CLIP
- catch up on recent self-supervised learning literature by year
- find code implementations for contrastive learning papers
- prepare a literature review on unsupervised representation learning

## When to choose
- you need a curated, organized reading list of contrastive learning research
- you want links to both papers and their code implementations
- you are surveying self-supervised or transfer learning techniques

## When to avoid
- you need runnable software or a library rather than a paper list
- you need tutorials or courses rather than research papers
- you need non-contrastive self-supervised methods covered comprehensively

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning
- domain: machine-learning, deep-learning, tutorials, awesome-lists
- platform: cross-platform
- tags: awesome-list, contrastive-learning, self-supervised-learning, representation-learning, papers, research, transfer-learning, unsupervised-learning

## Member repositories
- asheeshcric/awesome-contrastive-self-supervised-learning (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:19.418579+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 2026-08-30T04:49:58.838220+00:00, confidence not recorded.
  - readme: https://github.com/asheeshcric/awesome-contrastive-self-supervised-learning (fetched 2026-08-28T04:04:19.418579+00:00, sha 5d4afb53e20e)
- Data as of 2026-08-30T08:39:29.467469+00:00.