19 citations · 100 across the 46 of their papers we have counts for
11 papers · 1 filter
Visual prompting reimagined: The power of the Activation Prompts
Yihua Zhang, Hongkang Li, Yuguang Yao +5
Visual prompting (VP) has emerged as a popular method to repurpose pretrained vision models for adaptation to downstream tasks. Unlike conventional model fine-tuning techniques, VP…
Edit Away and My Face Will not Stay: Personal Biometric Defense against Malicious Generative Editing
Hanhui Wang, Yihua Zhang, Ruizheng Bai +3
Recent advancements in diffusion models have made generative image editing more accessible, enabling creative edits but raising ethical concerns, particularly regarding malicious e…
Moyun: A Diffusion-Based Model for Style-Specific Chinese Calligraphy Generation
Kaiyuan Liu, Jiahao Mei, Hengyu Zhang +3
Although Chinese calligraphy generation has achieved style transfer, generating calligraphy by specifying the calligrapher, font, and character style remains challenging. To addres…
Pruning then Reweighting: Towards Data-Efficient Training of Diffusion Models
Yize Li, Yihua Zhang, Sijia Liu +1
Despite the remarkable generation capabilities of Diffusion Models (DMs), conducting training and inference remains computationally expensive. Previous works have been devoted to a…
Defensive Unlearning with Adversarial Training for Robust Concept Erasure in Diffusion Models
Yimeng Zhang, Xin Chen, Jinghan Jia +6
Diffusion models (DMs) have achieved remarkable success in text-to-image generation, but they also pose safety risks, such as the potential generation of harmful content and copyri…
UnlearnCanvas: Stylized Image Dataset for Enhanced Machine Unlearning Evaluation in Diffusion Models
Yihua Zhang, Chongyu Fan, Yimeng Zhang +8
The technological advancements in diffusion models (DMs) have demonstrated unprecedented capabilities in text-to-image generation and are widely used in diverse applications. Howev…
Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective
Can Jin, Tianjin Huang, Yihua Zhang +4
The rapid development of large-scale deep learning models questions the affordability of hardware platforms, which necessitates the pruning to reduce their computational and memory…
To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now
Yimeng Zhang, Jinghan Jia, Xin Chen +5
The recent advances in diffusion models (DMs) have revolutionized the generation of realistic and complex images. However, these models also introduce potential safety hazards, suc…
Robust Mixture-of-Expert Training for Convolutional Neural Networks
Yihua Zhang, Ruisi Cai, Tianlong Chen +6
Sparsely-gated Mixture of Expert (MoE), an emerging deep model architecture, has demonstrated a great promise to enable high-accuracy and ultra-efficient model inference. Despite t…
A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion
Haomin Zhuang, Yihua Zhang, Sijia Liu
Despite the record-breaking performance in Text-to-Image (T2I) generation by Stable Diffusion, less research attention is paid to its adversarial robustness. In this work, we study…
Understanding and Improving Visual Prompting: A Label-Mapping Perspective
Aochuan Chen, Yuguang Yao, Pin-Yu Chen +2
We revisit and advance visual prompting (VP), an input prompting technique for vision tasks. VP can reprogram a fixed, pre-trained source model to accomplish downstream tasks in th…