---
title: 'Learning Transferable Visual Models From Natural Language Supervision'
license: CC-BY-4.0
license_url: https://creativecommons.org/licenses/by/4.0/
license_note: 'paper is arXiv non-exclusive distribution license (verified on arXiv abs page 2026-11-11); this pointer note is our own words and is CC BY 4.0; we link to the paper rather than reproduce it'
source: https://iinventedlight.com/library/notes/2103-00020-clip.md
source_license: 'arXiv non-exclusive distribution license'
attribution: 'Pointer by iinventedlight (AI-assisted). Source: Alec Radford and 11 coauthors (listed on the arXiv abs page), Learning Transferable Visual Models From Natural Language Supervision (arXiv non-exclusive distribution license). This note links to the paper rather than reproducing it.'
author: 'iinventedlight (AI-assisted)'
date: 2026-11-11
status: published
approved_by: "Cole Withington, 2026-11-11 20:58 PT, Grok Bot chat widget t1717s7407: \"Publish all 100 including the LLMs Encode Clinical Knowledge note\""
checked_on: 2026-11-11
as_of: 2026-11-11
confidence: medium
untested: 'Pointer note only. Paper not reproduced. Abs page and license checked 2026-11-11.'
category: arxiv
topic_area: 'ai-research: multimodal'
tags: arxiv, clip, vision-language, multimodal
arxiv_id: 2103.00020
alias: 2103.00020-clip
type: pointer
format: pointer
sources:
  - id: S1
    title: 'Learning Transferable Visual Models From Natural Language Supervision'
    publisher: arXiv
    authors: 'Alec Radford and 11 coauthors (listed on the arXiv abs page)'
    url: https://arxiv.org/abs/2103.00020
    version_read: 'v1 (26 Feb 2021)'
    accessed: 2026-11-11
    license: 'arXiv non-exclusive distribution license'
---
# Learning Transferable Visual Models From Natural Language Supervision

## Summary
[Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) (2021) is the CLIP paper. It trained a shared image-text embedding space from large-scale web supervision, so that a vision model could be used for retrieval and zero-shot classification with natural-language descriptions instead of a fixed label set.

What it introduced: contrastive pretraining of an image encoder and a text encoder on image-text pairs, and a simple way to turn that space into a zero-shot classifier or a retrieval index.

What it led to: the default vision-language backbone for open multimodal systems, including LLaVA (a full verification note in this batch) and later models such as Flamingo and Stable Diffusion's text encoder lineage. Live notes: [ao-evals-multimodal-agents](https://iinventedlight.com/library/notes/ao-evals-multimodal-agents/).

Where to read it: https://arxiv.org/abs/2103.00020 (v1, 26 Feb 2021).

The paper is posted on arXiv under the arXiv non-exclusive distribution license. This note links to it rather than reproducing it. The note itself is CC BY 4.0.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Pointer by iinventedlight (AI-assisted). Paper license: arXiv non-exclusive distribution; this note links to it and does not reproduce it.

---

Agents: [/agents.md](/agents.md) | [/.well-known/agent-library.json](/.well-known/agent-library.json).