---
title: "A Simple Framework for Contrastive Learning of — Chen 2020"
description: "Learning useful image features without labels usually meant hand-designed pretext tasks, special encoder architectures or memory banks of stored…"
authors: ["Ting Chen", "Simon Kornblith", "Mohammad Norouzi", "Geoffrey Hinton"]
year: 2020
venue: "arXiv:2002.05709"
source: "https://arxiv.org/abs/2002.05709"
doi: "10.48550/arxiv.2002.05709"
arxiv: "2002.05709"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/chen-2020-simple-framework-contrastive-learning-visual/"
---
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# A Simple Framework for Contrastive Learning of Visual Representations

**Authors:** Ting Chen, [Simon Kornblith](https://md.research.elamur.ai/free/en/authors/simon-kornblith/), [Mohammad Norouzi](https://md.research.elamur.ai/free/en/authors/mohammad-norouzi/), [Geoffrey Hinton](https://md.research.elamur.ai/free/en/authors/geoffrey-hinton/) · **Year:** 2020 · **Venue:** arXiv:2002.05709 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2002.05709) · [PDF](https://arxiv.org/pdf/2002.05709) · [DOI 10.48550/arxiv.2002.05709](https://doi.org/10.48550/arxiv.2002.05709) · [arXiv 2002.05709](https://arxiv.org/abs/2002.05709) · [OpenAlex](https://openalex.org/W3005680577)

**Citations:** 6894 citations (openalex-snapshot · 2026-10-14) · [877](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Learning useful image features without labels usually meant hand-designed pretext tasks, special encoder architectures or memory banks of stored embeddings. SimCLR, from Google Research, reduces contrastive self-supervised learning to a few parts. Each image gets two random augmentations. A standard ResNet encodes both views, and a small MLP projection head maps those features into a space where the NT-Xent loss pulls the paired views together. Every other image in the batch acts as a negative. Ablations on ImageNet ILSVRC-2012 show which choices matter: stacking several augmentations (especially random cropping with color distortion), putting a nonlinear head before the loss and discarding it after training, normalizing embeddings with a tuned temperature, and training with very large batches, for longer and with bigger networks. Combined, these choices give a linear classifier on frozen features as much top-1 accuracy as a fully supervised ResNet-50. The result shows that a simple recipe, which works with any architecture, can close the gap with supervised pretraining. That makes it useful wherever labelled data is scarce.

## Key points
- **Linear evaluation:** 76.5% top-1 on ImageNet with ResNet-50 (4×). That is about 7% better in relative terms than the previous best (CPCv2, Hénaff et al.).
- **Semi-supervised:** fine-tuning on just 1% of ImageNet labels (about 12.8 images per class) gives 85.8% top-5. That beats AlexNet while using 100× fewer labels.
- **Transfer learning:** across 12 natural image classification datasets, fine-tuned SimCLR matches or beats a strong supervised baseline on 10. The supervised model wins only on Pets and Flowers.
- **Training setup:** batch sizes from 256 to 8192 (up to 16,382 negatives per positive pair), the LARS optimizer, batch-norm statistics shared across all devices, and 32–128 Cloud TPU cores. By default it uses a 128-dimensional projection, batch size 4096 and 100 epochs.
- **Code:** released at github.com/google-research/simclr.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/)

## Cited by

Cited by

877 papers of our library cite it (of the 140673 whose bibliographies we read); 45 of them have a page here, 832 not yet.

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## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{chen2020simple,
  title = {{A Simple Framework for Contrastive Learning of Visual Representations}},
  author = {Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  year = {2020},
  doi = {10.48550/arxiv.2002.05709},
  eprint = {2002.05709},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2002.05709}
}
```

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