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# System prepended metadata

title: '[PAPER] A Simple framework for contrastive learning of visual representations'
tags: [paper]

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# [PAPER] A Simple Framework for contrastive learning of visual representations
![](https://hackmd.io/_uploads/HkOVfqcFh.png)


:::info
**Author** : Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton Proceedings of the 37th International Conference on Machine Learning

**Paper Link** : http://proceedings.mlr.press/v119/chen20j.html

**Code** : https://github.com/google-research/simclr
:::

## Content
* This paper present SimCLR:a simple framework for contrastive learning of visual representations. The authors systematically study the major components of their framework and show how it outperforms previous methods for self-supervised and semi-supervised learning on ImageNet. They also provide a comprehensive comparison of their design choices with those of previous work. The strength of this simple framework suggests that self-supervised learning remains undervalued.

