activity
20172021
most citedFeature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification

25 citations · 30 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CR20212 cited

Privacy-preserving Cloud-based DNN Inference

Shangyu Xie, Bingyu Liu, Yuan Hong

Deep learning as a service (DLaaS) has been intensively studied to facilitate the wider deployment of the emerging deep learning applications. However, DLaaS may compromise the pri…

cs.CV20202 cited

Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation

Bingyu Liu, Yuhong Guo, Jieping Ye +1

Recent domain adaptation methods have demonstrated impressive improvement on unsupervised domain adaptation problems. However, in the semi-supervised domain adaptation (SSDA) setti…

cs.CV20201 cited

Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data

Zhen Zhao, Bingyu Liu, Yuhong Guo +1

In this paper, we present our proposed ensemble model with batch spectral regularization and data blending mechanisms for the Track 2 problem of the cross-domain few-shot learning…

cs.CV202025 cited

Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification

Bingyu Liu, Zhen Zhao, Zhenpeng Li +3

In this paper, we propose a feature transformation ensemble model with batch spectral regularization for the Cross-domain few-shot learning (CD-FSL) challenge. Specifically, we pro…

cs.CR2017

Privacy Preserving and Collusion Resistant Energy Sharing

Yuan Hong, Han Wang, Shangyu Xie +1

Energy has been increasingly generated or collected by different entities on the power grid (e.g., universities, hospitals and householdes) via solar panels, wind turbines or local…