activity
20202022
most citedProgressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation

8 citations · 26 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG20222 cited

Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning

Shuang Dai, Fanlin Meng

Online federated learning (OFL) and online transfer learning (OTL) are two collaborative paradigms for overcoming modern machine learning challenges such as data silos, streaming d…

cs.CV20218 cited

Progressively Select and Reject Pseudo-labelled Samples for Open-Set Domain Adaptation

Qian Wang, Fanlin Meng, Toby P. Breckon

Domain adaptation solves image classification problems in the target domain by taking advantage of the labelled source data and unlabelled target data. Usually, the source and targ…

cs.LG20215 cited

FederatedNILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring based on Federated Deep Learning

Shuang Dai, Fanlin Meng, Qian Wang +1

Non-intrusive load monitoring (NILM), which usually utilizes machine learning methods and is effective in disaggregating smart meter readings from the household-level into applianc…

eess.SY20214 cited

Electrical peak demand forecasting- A review

Shuang Dai, Fanlin Meng, Hongsheng Dai +2

The power system is undergoing rapid evolution with the roll-out of advanced metering infrastructure and local energy applications (e.g. electric vehicles) as well as the increasin…

cs.CV20207 cited

Data Augmentation with norm-VAE for Unsupervised Domain Adaptation

Qian Wang, Fanlin Meng, Toby P. Breckon

We address the Unsupervised Domain Adaptation (UDA) problem in image classification from a new perspective. In contrast to most existing works which either align the data distribut…