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README.md

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+<div align="center">
+<h1> AutoView </h1>
+<span><font size="5", > Learning Self-Regularized Adversarial Views for Self-Supervised Vision Transformers
+ </font></span>
+</br>
+Tao Tang∗, Changlin Li∗, Guangrun Wang, Kaicheng Yu, Xiaojun Chang, Xiaodan Liang</a><sup><span>&#8224;</span></sup>
+
+(<span>*</span>: equal contribution, <span>&#8224;</span>: corresponding author)
+
+<div><a href="https://arxiv.org/pdf/2210.08458.pdf">[arXiv Preprint]</a></div> 
+
+## Introduction
+
+![Framework](./assets/autoview.png)
+
+We propose **AutoView**, a self-regularized adversarial AutoAugment method, to learn views for self-supervised vision transformers. 
+
+* First, we reduce the search cost of AutoView to nearly zero by learning views and network parameters simultaneously in a single forward-backward step, minimizing and maximizing the mutual information among different augmented views, respectively. 
+* Then, to avoid information collapse caused by the lack of label supervision, we propose a self-regularized loss term to guarantee the information propagation. 
+* Additionally, we present a curated augmentation policy search space for self-supervised learning, by modifying the generally used search space designed for supervised learning. 
+* On ImageNet, our AutoView achieves remarkable improvement over RandAug baseline (+10.2% k-NN accuracy), and consistently outperforms *sota* manually tuned view policy by a clear margin. Extensive experiments show that AutoView pretraining also benefits downstream tasks and improves model robustness.
+
+
+
+## Visualization
+
+![vis](./assets/vis.png)
+
+## Getting Started
+
+````bash
+git clone https://github.com/Trent-tangtao/AutoView.git
+````
+
+This is a preliminary release. We have not carefully organized everything now.
+
+
+
+## Citation
+
+If you find AutoView is useful in your research or applications, please consider giving us a star &#127775; and citing it by the following BibTeX entry.
+
+```bibtex
+@article{tang2022learning,
+ title={Learning Self-Regularized Adversarial Views for Self-Supervised Vision Transformers}, 
+ author={Tao Tang and Changlin Li and Guangrun Wang and Kaicheng Yu and Xiaojun Chang and Xiaodan Liang},
+ journal={arXiv preprint arXiv:2210.08458},
+ year={2022}
+}
+```