scholarly journals Testing Convolutional Neural Networks for finding strong gravitational lenses in KiDS

2018 ◽  
Author(s):  
C E Petrillo ◽  
C Tortora ◽  
S Chatterjee ◽  
G Vernardos ◽  
L V E Koopmans ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gravitational Lenses

scholarly journals Fast automated analysis of strong gravitational lenses with convolutional neural networks

Nature ◽  
2017 ◽  
Vol 548 (7669) ◽  
pp. 555-557 ◽  
Author(s):  
Yashar D. Hezaveh ◽  
Laurence Perreault Levasseur ◽  
Philip J. Marshall
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Automated Analysis ◽  
Gravitational Lenses

scholarly journals An Extended Catalog of Galaxy–Galaxy Strong Gravitational Lenses Discovered in DES Using Convolutional Neural Networks

2019 ◽  
Vol 243 (1) ◽  
pp. 17 ◽  
Author(s):  
C. Jacobs ◽  
T. Collett ◽  
K. Glazebrook ◽  
E. Buckley-Geer ◽  
H. T. Diehl ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gravitational Lenses

scholarly journals Finding strong gravitational lenses in the Kilo Degree Survey with Convolutional Neural Networks

2017 ◽  
Vol 472 (1) ◽  
pp. 1129-1150 ◽  
Author(s):  
C. E. Petrillo ◽  
C. Tortora ◽  
S. Chatterjee ◽  
G. Vernardos ◽  
L. V. E. Koopmans ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Gravitational Lenses

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Convolutional Neural Networks for Underwater Pipeline Segmentation using Imperfect Datasets

2021 ◽  
Author(s):  
Edgar Medina ◽  
Roberto Campos ◽  
Jose Gabriel R. C. Gomes ◽  
Mariane R. Petraglia ◽  
Antonio Petraglia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Underwater Pipeline
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