scholarly journals DropAll: Generalization of Two Convolutional Neural Network Regularization Methods

2014 ◽  
pp. 282-289 ◽  
Author(s):  
Xavier Frazão ◽  
Luís A. Alexandre
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Regularization Methods

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

No-Reference Utility Estimation with a Convolutional Neural Network

2018 ◽  
Vol 2018 (9) ◽  
pp. 202-1-202-6 ◽  
Author(s):  
Edward T. Scott ◽  
Sheila S. Hemami
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Utility Estimation

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

Hierarchical Auto-associative Polynomial Convolutional Neural Network (HAP-CNN)

2018 ◽  
Vol 2018 (10) ◽  
pp. 338-1-338-6
Author(s):  
Patrick Martell ◽  
Vijayan Asari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Defect Detection of Axle Box Cover Device Fixing Bolts in Metro Based on Convolutional Neural Network

2020 ◽  
Author(s):  
Yao Yang ◽  
Yuanjiang Hu ◽  
Lingling Chen ◽  
Xiaoman Liu ◽  
Na Qin ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Defect Detection

Convolutional Neural Network for Determining Sensitivity Matrix in ETI

2020 ◽  
Author(s):  
Haitao Ma ◽  
Shihong Yue ◽  
Jian Lu ◽  
Sidolla Yem ◽  
Huaxiang Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sensitivity Matrix

Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

2019 ◽  
Author(s):  
Seoin Back ◽  
Junwoong Yoon ◽  
Nianhan Tian ◽  
Wen Zhong ◽  
Kevin Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
High Throughput Screening ◽  
Binding Energies ◽  
Surface Structures ◽  
Voronoi Polyhedra ◽  
Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

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