---
license: mit
---
## CACR checkpoint repository

### For implementation details, please refer to our [Github Repo](https://github.com/JegZheng/CACR-SSL)


#### ImageNet pretrained, performance of linear classification on ImageNet
<table><tbody>
<!-- START TABLE -->
<!-- TABLE HEADER -->
<th align="left">model</th>
<th align="left">pretrain<br/>epochs</th>
<th align="left">linear<br/>acc</th>
<!-- TABLE BODY -->
<tr>
<td align="left">ResNet50</td>
<td align="left">1000</td>
<td align="left">74.7</td>
</tr>
<tr>
<td align="left">ViT-Base</td>
<td align="left">300</td>
<td align="left">77.1</td>
</tr>
</tbody></table>

#### ImageNet pretrained, performance of linear classification on 20 Image in the Wild datasets
Please feel free to check our learned representation performance in [Image in the Wild Challenge](https://eval.ai/web/challenges/challenge-page/1832/leaderboard/4301).


### Citation
Please cite our work if you find it is helpful. Thank you!
```
@article{
  zheng2023contrastive,
  title={Contrastive Attraction and Contrastive Repulsion for Representation Learning},
  author={Huangjie Zheng and Xu Chen and Jiangchao Yao and Hongxia Yang and Chunyuan Li and Ya Zhang and Hao Zhang and Ivor Tsang and Jingren Zhou and Mingyuan Zhou},
  journal={Transactions on Machine Learning Research},
  issn={2835-8856},
  year={2023},
  url={https://openreview.net/forum?id=f39UIDkwwc},
}
```