10 citations · 20 across the 5 of their papers we have counts for
5 papers
To transfer or not transfer: Unified transferability metric and analysis
Qianshan Zhan, Xiao-Jun Zeng
In transfer learning, transferability is one of the most fundamental problems, which aims to evaluate the effectiveness of arbitrary transfer tasks. Existing research focuses on cl…
On Fine-Tuned Deep Features for Unsupervised Domain Adaptation
Qian Wang, Toby P. Breckon
Prior feature transformation based approaches to Unsupervised Domain Adaptation (UDA) employ the deep features extracted by pre-trained deep models without fine-tuning them on the…
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…
Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation
Qian Wang, Toby P. Breckon
Heterogeneous Domain Adaptation (HDA) addresses the transfer learning problems where data from the source and target domains are of different modalities (e.g., texts and images) or…
Unsupervised Domain Adaptation via Structured Prediction Based Selective Pseudo-Labeling
Qian Wang, Toby P. Breckon
Unsupervised domain adaptation aims to address the problem of classifying unlabeled samples from the target domain whilst labeled samples are only available from the source domain…