Façade defects classification from imbalanced dataset using meta learning‐based convolutional neural network-Reference-Cited by-同舟云学术

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Façade defects classification from imbalanced dataset using meta learning‐based convolutional neural network

Published:2020-06-10 Issue:12 Volume:35 Page:1403-1418
ISSN:1093-9687
Container-title:Computer-Aided Civil and Infrastructure Engineering
language:en
Short-container-title:Computer‐Aided Civil and Infrastructure Engineering

Author:

Guo Jingjing¹,Wang Qian¹,Li Yiting²,Liu Pengkun³

Affiliation:

1. Department of Building, School of Design and Environment National University of Singapore Singapore Singapore

2. Department of Electrical and Computer Engineering, Faculty of Engineering National University of Singapore Singapore Singapore

3. School of Civil Engineering Chongqing University Chongqing China

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Civil and Structural Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/mice.12578

Reference43 articles.

1. Enhanced probabilistic neural network with local decision circles: A robust classifier

2. An improved algorithm for neural network classification of imbalanced training sets

3. Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection

4. A systematic study of the class imbalance problem in convolutional neural networks

5. Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

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