activity
20192022
most citedUnsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.NI20221 cited

Sizing up the Batteries: Modelling of Energy-Harvesting Sensor Nodes in a Delay Tolerant Network

Jeremiah D. Deng

For energy-harvesting sensor nodes, rechargeable batteries play a critical role in sensing and transmissions. By coupling two simple Markovian queue models in a delay-tolerant netw…

eess.IV2021

Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations

Jinyong Hou, Xuejie Ding, Jeremiah D. Deng

Semantic segmentation based on deep learning methods can attain appealing accuracy provided large amounts of annotated samples. However, it remains a challenging task when only lim…

cs.LG2020

Cross-Domain Latent Modulation for Variational Transfer Learning

Jinyong Hou, Jeremiah D. Deng, Stephen Cranefield +1

We propose a cross-domain latent modulation mechanism within a variational autoencoders (VAE) framework to enable improved transfer learning. Our key idea is to procure deep repres…

eess.SP2020

Resting-state EEG sex classification using selected brain connectivity representation

Jean Li, Jeremiah D. Deng, Divya Adhia +1

Effective analysis of EEG signals for potential clinical applications remains a challenging task. So far, the analysis and conditioning of EEG have largely remained sex-neutral. Th…

cs.CV20192 cited

Unsupervised Domain Adaptation using Deep Networks with Cross-Grafted Stacks

Jinyong Hou, Xuejie Ding, Jeremiah D. Deng +1

Current deep domain adaptation methods used in computer vision have mainly focused on learning discriminative and domain-invariant features across different domains. In this paper,…