refine

src/display/about.py

@@ -13,9 +13,9 @@ The rapid advancement of diffusion models (DMs) has not only transformed various
 
 To address them, we enhance the evaluation metrics for MU, including the introduction of an often-overlooked retainability measurement for DMs post-unlearning. Additionally, we introduce <strong>UnlearnCanvas, a comprehensive high-resolution stylized image dataset</strong> that facilitates us to evaluate the unlearning of artistic painting styles in conjunction with associated image objects. 
 
-We show that this dataset plays a pivotal role in establishing a standardized and automated evaluation framework for MU techniques on DMs, featuring 7 quantitative metrics to address various aspects of unlearning effectiveness. Through extensive experiments, we benchmark 5 state-of- the-art MU methods, revealing novel insights into their pros and cons, and the underlying unlearning mechanisms. Furthermore, we demonstrate the potential of UnlearnCanvas to benchmark other generative modeling tasks, such as style transfer.
+We show that this dataset plays a pivotal role in establishing <strong>a standardized and automated evaluation framework for MU techniques on DMs</strong>, featuring <strong>7 quantitative metrics</strong> to address various aspects of unlearning effectiveness. Through extensive experiments, we benchmark <strong>5 state-of- the-art MU methods</strong>, revealing novel insights into their pros and cons, and the underlying unlearning mechanisms. Furthermore, we demonstrate the potential of UnlearnCanvas to benchmark other generative modeling tasks, such as style transfer.
 
-\[Related Benchmarks\]
+\[Other Related Benchmarks\]
 - [<strong>UnlearnDiff Benchmark</strong>](https://github.com/OPTML-Group/Diffusion-MU-Attack): an evaluation benchmark built upon adversarial attacks (also referred to as adversarial prompts), in order to discern the <strong>trustworthiness of these safety-driven unlearned DMs</strong>. 
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