Synthetic Defect Generation for Display Front-of-Screen Quality Inspection: A Survey
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Abstract (arXiv)
Display front-of-screen (FOS) quality inspection is essential for the mass production of displays in the manufacturing process. However, the severe imbalanced data, especially the limited number of defect samples, has been a long-standing problem that hinders the successful application of deep learning algorithms. Synthetic defect data generation can help address this issue. This paper reviews the state-of-the-art synthetic data generation methods and the evaluation metrics that can potentially be applied to display FOS quality inspection tasks.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 37 references that are in the map. First posted on arXiv 3 Mar 2022.
Builds on
14 of 37 references.
References · 14
- 2022Generative Adversarial Networkscited by 5,603
- 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
- 2014Conditional Generative Adversarial Netscited by 2,152
- 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
- 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 858
- 2018Multimodal Unsupervised Image-to-Image Translationcited by 795
- 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 562
- 2018Pros and Cons of GAN Evaluation Measurescited by 224
- 2020A Review on Generative Adversarial Networks: Algorithms, Theory, and Applicationscited by 209
- 2021Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspectioncited by 77
- 2018Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Datacited by 76
- 2020Video Generative Adversarial Networks: A Reviewcited by 33
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.