7 citations · 9 across the 5 of their papers we have counts for
5 papers
Variational Transfer Learning using Cross-Domain Latent Modulation
Jinyong Hou, Jeremiah D. Deng, Stephen Cranefield +1
To successfully apply trained neural network models to new domains, powerful transfer learning solutions are essential. We propose to introduce a novel cross-domain latent modulati…
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…
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…
Deep Adversarial Transition Learning using Cross-Grafted Generative Stacks
Jinyong Hou, Xuejie Ding, Stephen Cranefield +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,…
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,…