activity
20162023
most citedAUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss

195 citations · 759 across the 30 of their papers we have counts for

collaborators

30 papers

cs.CL2023★ 5 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.CV2023★ 4 cited

Harnessing the Spatial-Temporal Attention of Diffusion Models for High-Fidelity Text-to-Image Synthesis

Qiucheng Wu, Yujian Liu, Handong Zhao +4

Diffusion-based models have achieved state-of-the-art performance on text-to-image synthesis tasks. However, one critical limitation of these models is the low fidelity of generate…

cs.CV2023★ 17 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…

cs.CL2022★ 1 cited

TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization

Bairu Hou, Jinghan Jia, Yihua Zhang +4

Robustness evaluation against adversarial examples has become increasingly important to unveil the trustworthiness of the prevailing deep models in natural language processing (NLP…

cs.CV2022★ 3 cited

Uncovering the Disentanglement Capability in Text-to-Image Diffusion Models

Qiucheng Wu, Yujian Liu, Handong Zhao +6

Generative models have been widely studied in computer vision. Recently, diffusion models have drawn substantial attention due to the high quality of their generated images. A key…

cs.LG2022★ 6 cited

Fairness Reprogramming

Guanhua Zhang, Yihua Zhang, Yang Zhang +4

Despite a surge of recent advances in promoting machine Learning (ML) fairness, the existing mainstream approaches mostly require retraining or finetuning the entire weights of the…