research
∙
05/09/2021
Binarized Weight Error Networks With a Transition Regularization Term
This paper proposes a novel binarized weight network (BT) for a resource...
share
research
∙
12/12/2020
Spectral Unmixing With Multinomial Mixture Kernel and Wasserstein Generative Adversarial Loss
This study proposes a novel framework for spectral unmixing by using 1D ...
research
∙
06/08/2020
Cross-Domain Segmentation with Adversarial Loss and Covariate Shift for Biomedical Imaging
Despite the widespread use of deep learning methods for semantic segment...
research
∙
01/17/2020
CHAOS Challenge – Combined (CT-MR) Healthy Abdominal Organ Segmentation
Segmentation of abdominal organs has been a comprehensive, yet unresolve...
research
∙
11/28/2018
Automatic Liver Segmentation with Adversarial Loss and Convolutional Neural Network
Automatic segmentation of medical images is among most demanded works in...
research
∙
08/03/2018
Improved Deep Spectral Convolution Network For Hyperspectral Unmixing With Multinomial Mixture Kernel and Endmember Uncertainty
In this study, we propose a novel framework for hyperspectral unmixing b...
research
∙
08/03/2018
Late Fusion of Local Indexing and Deep Feature Scores for Fast Image-to-Video Search on Large-Scale Databases
Low cost visual representation and fast query-by-example content search ...
research
∙
06/22/2018
KinshipGAN: Synthesizing of Kinship Faces From Family Photos by Regularizing a Deep Face Network
In this paper, we propose a kinship generator network that can synthesiz...
research
∙
06/22/2018
Deep Spectral Convolution Network for HyperSpectral Unmixing
In this paper, we propose a novel hyperspectral unmixing technique based...
research
∙
01/26/2018
Cloud Detection From RGB Color Remote Sensing Images With Deep Pyramid Networks
Cloud detection from remotely observed data is a critical pre-processing...
research
∙
08/24/2017
Relaxed Spatio-Temporal Deep Feature Aggregation for Real-Fake Expression Prediction
Frame-level visual features are generally aggregated in time with the te...
research
∙
08/06/2017