activity
20232026
most citedAdversarial Training: A Survey

5 citations · 5 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CR2026

CoreUnlearn: Rethinking Concept Unlearning through Disentangled Component-Level Erasure in Text-guided Diffusion Models

Mengnan Zhao, Lihe Zhang, Baocai Yin

Text guided diffusion models have revolutionized image synthesis but also raise ethical concerns, such as privacy violation and harmful content generation. To mitigate these issues…

cs.LG2026

Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training

Mengnan Zhao, Lihe Zhang, Tianhang Zheng +2

Fast Adversarial Training (FAT) has attracted significant attention due to its efficiency in enhancing neural network robustness against adversarial attacks. However, FAT is prone…

cs.LG2024

AdvAnchor: Enhancing Diffusion Model Unlearning with Adversarial Anchors

Mengnan Zhao, Lihe Zhang, Xingyi Yang +2

Security concerns surrounding text-to-image diffusion models have driven researchers to unlearn inappropriate concepts through fine-tuning. Recent fine-tuning methods typically ali…

cs.LG2024★ 5 cited

Adversarial Training: A Survey

Mengnan Zhao, Lihe Zhang, Jingwen Ye +3

Adversarial training (AT) refers to integrating adversarial examples -- inputs altered with imperceptible perturbations that can significantly impact model predictions -- into the…

cs.LG2024

Catastrophic Overfitting: A Potential Blessing in Disguise

Mengnan Zhao, Lihe Zhang, Yuqiu Kong +1

Fast Adversarial Training (FAT) has gained increasing attention within the research community owing to its efficacy in improving adversarial robustness. Particularly noteworthy is…

cs.LG2024

Separable Multi-Concept Erasure from Diffusion Models

Mengnan Zhao, Lihe Zhang, Tianhang Zheng +2

Large-scale diffusion models, known for their impressive image generation capabilities, have raised concerns among researchers regarding social impacts, such as the imitation of co…

cs.CV2023

EipFormer: Emphasizing Instance Positions in 3D Instance Segmentation

Mengnan Zhao, Lihe Zhang, Yuqiu Kong +1

3D instance segmentation plays a crucial role in comprehending 3D scenes. Despite recent advancements in this field, existing approaches exhibit certain limitations. These methods…