A systemic comparison between using augmented data and synthetic data as means of enhancing wafermap defect classification-Reference-Cited by-同舟云学术

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A systemic comparison between using augmented data and synthetic data as means of enhancing wafermap defect classification

Published:2023-02 Issue: Volume:145 Page:103809
ISSN:0166-3615
Container-title:Computers in Industry
language:en
Short-container-title:Computers in Industry

Author:

Alqudah Rajaa,Al-Mousa Amjed A.,Abu Hashyeh Yazan,Alzaibaq Omar Z.

Publisher

Elsevier BV

Subject

General Engineering,General Computer Science

Reference22 articles.

1. Wafer map defect patterns classification using deep selective learning;Alawieh,2020

2. wafermap wm-811k dataset;Anon,2022

3. The advantages and limitations of synthetic data;Anon,2022

4. Google. Welcome to colaboratory!;Anon,2022

5. Comprehensive guide to multiclass classification metrics;Anon,2022

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1. Advancing deep learning-based acoustic leak detection methods towards application for water distribution systems from a data-centric perspective;Water Research;2024-09

2. Performance Assessment of Object Detection Models Trained with Synthetic Data: A Case Study on Electrical Equipment Detection;Sensors;2024-06-28

3. Sparse deep encoded features with enhanced sinogramic red deer optimization for fault inspection in wafer maps;Journal of Intelligent Manufacturing;2024-05-20

4. Generative Model-Driven Synthetic Training Image Generation: An Approach to Cognition in Railway Defect Detection;Cognitive Computation;2024-05-17

5. Characterization and depth detection of internal delamination defects in CFRP based on line laser scanning infrared thermography;Structural Health Monitoring;2024-02-05

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