Home /
Research
Showing 775 - 780 / 897
Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack Detectors
YukiInoue, HirotoNagayoshi....
Published date-11/04/2020
Automatic crack detection is a critical task that has the potential to drastically reduce labor-intensive building and road inspections currently being done manually. Recent studies in this field have significantly …
A deep learning classifier for local ancestry inference
MatthewAguirre, JanSokol, GuhanVenkataraman....
Published date-11/04/2020
SemanticSegmentation
Local ancestry inference (LAI) identifies the ancestry of each segment of an individual's genome and is an important step in medical and population genetic studies of diverse cohorts. Several techniques …
EAdam Optimizer: How $ε$ Impact Adam
WeiYuan, Kai-XinGao....
Published date-11/04/2020
Many adaptive optimization methods have been proposed and used in deep learning, in which Adam is regarded as the default algorithm and widely used in many deep learning frameworks. Recently, …
PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents
RyoFujii, MasatoMita, KaoriAbe....
Published date-11/04/2020
MachineTranslation
Neural Machine Translation (NMT) has shown drastic improvement in its quality when translating clean input, such as text from the news domain. However, existing studies suggest that NMT still struggles …
Low cost enhanced security face recognition with stereo cameras
BielTuraVecino, MartíCobos, PhilippeSalembier....
Published date-11/04/2020
FaceRecognition
This article explores a face recognition alternative which seeks to contribute to resolve current security vulnerabilities in most recognition architectures. Current low cost facial authentication software in the market can …
A Two-Stage Cascade Model with Variational Autoencoders and Attention Gates for MRI Brain Tumor Segmentation
ChenggangLyu, HaiShu....
Published date-11/04/2020
BrainTumorSegmentation, TumorSegmentation
Automatic MRI brain tumor segmentation is of vital importance for the disease diagnosis, monitoring, and treatment planning. In this paper, we propose a two-stage encoder-decoder based model for brain tumor …