211 citations · 1.2k across the 80 of their papers we have counts for
6 papers · 1 filter
TripletGAN: Training Generative Model with Triplet Loss
Gongze Cao, Yezhou Yang, Jie Lei +3
As an effective way of metric learning, triplet loss has been widely used in many deep learning tasks, including face recognition and person-ReID, leading to many states of the art…
Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification
Weijian Deng, Liang Zheng, Qixiang Ye +3
Person re-identification (re-ID) models trained on one domain often fail to generalize well to another. In our attempt, we present a "learning via translation" framework. In the ba…
Occlusion Aware Unsupervised Learning of Optical Flow
Yang Wang, Yi Yang, Zhenheng Yang +3
It has been recently shown that a convolutional neural network can learn optical flow estimation with unsupervised learning. However, the performance of the unsupervised methods st…
Convolutional Neural Networks: Ensemble Modeling, Fine-Tuning and Unsupervised Semantic Localization for Intraoperative CLE Images
Mohammadhassan Izadyyazdanabadi, Evgenii Belykh, Michael Mooney +5
Confocal laser endomicroscopy (CLE) is an advanced optical fluorescence technology undergoing assessment for applications in brain tumor surgery. Despite its promising potential, i…
On the Importance of Consistency in Training Deep Neural Networks
Chengxi Ye, Yezhou Yang, Cornelia Fermuller +1
We explain that the difficulties of training deep neural networks come from a syndrome of three consistency issues. This paper describes our efforts in their analysis and treatment…
Neural Style Transfer: A Review
Yongcheng Jing, Yezhou Yang, Zunlei Feng +3
The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style.…