activity
20182023
most citedHierarchically Fair Federated Learning

43 citations · 110 across the 14 of their papers we have counts for

collaborators

17 papers

cs.CL2023

Multi-teacher Distillation for Multilingual Spelling Correction

Jingfen Zhang, Xuan Guo, Sravan Bodapati +1

Accurate spelling correction is a critical step in modern search interfaces, especially in an era of mobile devices and speech-to-text interfaces. For services that are deployed ar…

cs.LG20224 cited

Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks

Jianan Zhou, Jianing Zhu, Jingfeng Zhang +4

Adversarial training (AT) with imperfect supervision is significant but receives limited attention. To push AT towards more practical scenarios, we explore a brand new yet challeng…

cs.CV2022

Diverse Instance Discovery: Vision-Transformer for Instance-Aware Multi-Label Image Recognition

Yunqing Hu, Xuan Jin, Yin Zhang +5

Previous works on multi-label image recognition (MLIR) usually use CNNs as a starting point for research. In this paper, we take pure Vision Transformer (ViT) as the research base…

cs.SD20223 cited

WaveFuzz: A Clean-Label Poisoning Attack to Protect Your Voice

Yunjie Ge, Qian Wang, Jingfeng Zhang +3

People are not always receptive to their voice data being collected and misused. Training the audio intelligence systems needs these data to build useful features, but the cost for…

cs.LG20222 cited

On the Effectiveness of Adversarial Training against Backdoor Attacks

Yinghua Gao, Dongxian Wu, Jingfeng Zhang +4

DNNs' demand for massive data forces practitioners to collect data from the Internet without careful check due to the unacceptable cost, which brings potential risks of backdoor at…

eess.IV20221 cited

Towards Adversarially Robust Deep Image Denoising

Hanshu Yan, Jingfeng Zhang, Jiashi Feng +2

This work systematically investigates the adversarial robustness of deep image denoisers (DIDs), i.e, how well DIDs can recover the ground truth from noisy observations degraded by…