Update README.md

README.md

@@ -1,44 +1,58 @@
-
-
-# Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models
-###  [Project Website]() | [Arxiv Preprint](https://arxiv.org/abs/2405.15234) | [Fine-tuned Weights](https://drive.google.com/drive/folders/1Nf-EJ2W3CsZwpc5blZFi7tm7o1wEiTg4?usp=sharing) | [Demo]() <br>
-Our proposed robust unlearning framework, AdvUnlearn, enhances diffusion models' safety by robustly erasing unwanted concepts through adversarial training, achieving an optimal balance between concept erasure and image generation quality. 
-
-
-
-
-
-## Baselines
-
-| DM Unlearning Methods | Nudity  | Van Gogh  | Objects |
-|:-------|:----:|:-------:| :-------:|
-| [ESD](https://github.com/rohitgandikota/erasing) (Erased Stable Diffusion)  | ✅  | ✅   | ✅ 
-| [FMN](https://github.com/SHI-Labs/Forget-Me-Not) (Forget-Me-Not)  | ✅ | ✅   | ✅ 
-| [AC](https://github.com/nupurkmr9/concept-ablation) (Ablating Concepts)  | ❌ | ✅   | ❌ 
-| [UCE](https://github.com/rohitgandikota/unified-concept-editing) (Unified Concept Editing)  | ✅  |  ✅  |  ❌
-| [SalUn](https://github.com/OPTML-Group/Unlearn-Saliency) (Saliency Unlearning)  | ✅  | ❌ |  ✅ 
-| [SH](https://github.com/JingWu321/Scissorhands_ex) (ScissorHands)  | ✅  | ❌  | ✅ 
-| [ED](https://github.com/JingWu321/EraseDiff) (EraseDiff)  | ✅  | ❌  | ✅ 
-| [SPM](https://github.com/Con6924/SPM) (concept-SemiPermeable Membrane)   | ✅  | ✅   | ✅ 
-| **AdvUnlearn (Ours)**  | ✅  | ✅   |  ✅ 
-
-
-<br>
-
-
-## Cite Our Work
-The preprint can be cited as follows:
-```
-@misc{zhang2024defensive,
-      title={Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models}, 
-      author={Yimeng Zhang and Xin Chen and Jinghan Jia and Yihua Zhang and Chongyu Fan and Jiancheng Liu and Mingyi Hong and Ke Ding and Sijia Liu},
-      year={2024},
-      eprint={2405.15234},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```
-
----
-license: cc-by-4.0
+---
+license: cc-by-4.0
+language:
+- en
+tags:
+- art
+- unlearned_textencoder
+- safe_diffusion
+- adversarial_training
+- defensive_unlearning
+---
+
+
+# Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models
+###  [Project Website]() | [Arxiv Preprint](https://arxiv.org/abs/2405.15234) | [Fine-tuned Weights](https://drive.google.com/drive/folders/1Nf-EJ2W3CsZwpc5blZFi7tm7o1wEiTg4?usp=sharing) | [Demo]() <br>
+Our proposed robust unlearning framework, AdvUnlearn, enhances diffusion models' safety by robustly erasing unwanted concepts through adversarial training, achieving an optimal balance between concept erasure and image generation quality. 
+
+
+
+<div align="center">
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/63e44e62789dcaae43c865d9/vad9l9ME0KD0OJKYLcFun.png" />
+</div>
+
+
+## Baselines
+
+| DM Unlearning Methods | Nudity  | Van Gogh  | Objects |
+|:-------|:----:|:-------:| :-------:|
+| [ESD](https://github.com/rohitgandikota/erasing) (Erased Stable Diffusion)  | ✅  | ✅   | ✅ 
+| [FMN](https://github.com/SHI-Labs/Forget-Me-Not) (Forget-Me-Not)  | ✅ | ✅   | ✅ 
+| [AC](https://github.com/nupurkmr9/concept-ablation) (Ablating Concepts)  | ❌ | ✅   | ❌ 
+| [UCE](https://github.com/rohitgandikota/unified-concept-editing) (Unified Concept Editing)  | ✅  |  ✅  |  ❌
+| [SalUn](https://github.com/OPTML-Group/Unlearn-Saliency) (Saliency Unlearning)  | ✅  | ❌ |  ✅ 
+| [SH](https://github.com/JingWu321/Scissorhands_ex) (ScissorHands)  | ✅  | ❌  | ✅ 
+| [ED](https://github.com/JingWu321/EraseDiff) (EraseDiff)  | ✅  | ❌  | ✅ 
+| [SPM](https://github.com/Con6924/SPM) (concept-SemiPermeable Membrane)   | ✅  | ✅   | ✅ 
+| **AdvUnlearn (Ours)**  | ✅  | ✅   |  ✅ 
+
+
+<br>
+
+
+## Cite Our Work
+The preprint can be cited as follows:
+```
+@misc{zhang2024defensive,
+      title={Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models}, 
+      author={Yimeng Zhang and Xin Chen and Jinghan Jia and Yihua Zhang and Chongyu Fan and Jiancheng Liu and Mingyi Hong and Ke Ding and Sijia Liu},
+      year={2024},
+      eprint={2405.15234},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+---
+license: cc-by-4.0
 ---