most citedTowards Coherent Image Inpainting Using Denoising Diffusion Implicit Models

17 citations · 36 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL20244 cited

Advancing the Robustness of Large Language Models through Self-Denoised Smoothing

Jiabao Ji, Bairu Hou, Zhen Zhang +7

Although large language models (LLMs) have achieved significant success, their vulnerability to adversarial perturbations, including recent jailbreak attacks, has raised considerab…

cs.CV20241 cited

DiffGaze: A Diffusion Model for Continuous Gaze Sequence Generation on 360° Images

Chuhan Jiao, Yao Wang, Guanhua Zhang +3

We present DiffGaze, a novel method for generating realistic and diverse continuous human gaze sequences on 360° images based on a conditional score-based denoising diffusion model…

cs.CV20233 cited

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…

cs.CL20235 cited

Certified Robustness for Large Language Models with Self-Denoising

Zhen Zhang, Guanhua Zhang, Bairu Hou +5

Although large language models (LLMs) have achieved great success in vast real-world applications, their vulnerabilities towards noisy inputs have significantly limited their uses,…

cs.CV20236 cited

Improving Diffusion Models for Scene Text Editing with Dual Encoders

Jiabao Ji, Guanhua Zhang, Zhaowen Wang +4

Scene text editing is a challenging task that involves modifying or inserting specified texts in an image while maintaining its natural and realistic appearance. Most previous appr…

cs.CV202317 cited

Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models

Guanhua Zhang, Jiabao Ji, Yang Zhang +3

Image inpainting refers to the task of generating a complete, natural image based on a partially revealed reference image. Recently, many research interests have been focused on ad…