Granted Patent
B2
US 12,718,535 · App. 18/533,221 · Granted Aug 25, 2026
Vector bypass for generative adversarial image segmentation
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Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST
Recorded Dec 8, 2023
From: QIU, HAOXIANG; OSOGAMI, TAKAYUKI; KATSUKI, TAKAYUKI; SAKAI, TOMOYA; INOUE, TADANOBU
To: INTERNATIONAL BUSINESS MACHINES CORPORATION
Reel/Frame 065806/0853 →
Continuity (1)
References Cited (32)
CN 112734764A
· 2021
[cited by applicant]
CN 112991353A
· 2021
[cited by applicant]
CN 113204522A
· 2021
[cited by applicant]
EP 3576100A1
· 2019
[cited by applicant]
Joyce et al., “Deep Multi-Class Segmentation Without Ground-Truth Labels,” 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Published: Jul. 11, 2022, https://openreview.net/forum?id=S11Xr-3iM, 9 pages. …
[cited by examiner]
Arjovsky et al., “Wasserstein GAN,” arXiv:1701.07875v3 [stat.ML] Dec. 6, 2017, 32 pages.
[cited by applicant]
Chow et al. “Anomaly detection of defects on concrete structures with the convolutional autoencoder,” Advanced Engineering Informatics, vol. 45, Aug. 2020, 101105, https://www.sciencedirect.com/science/article/abs/pii/S…
[cited by applicant]
Dosovitskiy et al., “An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale,” arXiv:2010.11929v2 [cs.CV] Jun. 3, 2021, 22 pages.
[cited by applicant]
Duan et al., “Unsupervised Pixel-level Crack Detection Based on Generative Adversarial Network,” ACM, ICMSSP2020, May 28-30, 2020, https://dl.acm.org/doi/10.1145/3404716.3404720, 5 pages.
[cited by applicant]
Ganin et al., “Domain-Adversarial Training of Neural Networks,” Journal of Machine Learning Research 17 (2016), arXiv:1505.07818v4 [stat.ML] May 26, 2016, 35 pages.
[cited by applicant]
Goodfellow et al., “Generative Adversarial Nets,” arXiv:1406.2661v1 [stat.ML] Jun. 10, 2014, 9 pages.
[cited by applicant]
Gulrajani et al., “Improved Training of Wasserstein GANs,” arXiv:1704.00028v3 [cs.LG] Dec. 25, 2017, 20 pages.
[cited by applicant]
Hung et al., “Adversarial Learning for Semi-Supervised Semantic Segmentation,” arXiv:1802.07934v2 [cs.CV] Jul. 24, 2018, 17 pages.
[cited by applicant]
Joyce et al., “Deep Multi-Class Segmentation Without Ground-Truth Labels,” 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), Published: Jul. 11, 2022, https://openreview.net/forum?id=S11Xr-3iM, 9 pages.
[cited by applicant]
Lee, “Pseudo-label : The simple and efficient semi-supervised learning method for deep neural networks,” ICML 2013 Workshop : Challenges in Representation Learning (WREPL), Jul. 2013, 7 pages.
[cited by applicant]
Lin et al., “Focal Loss for Dense Object Detection,” arXiv:1708.02002v2 [cs.CV] Feb. 7, 2018, 10 pages.
[cited by applicant]
Liu et al., “DeepCrack: A deep hierarchical feature learning architecture for crack segmentation,” Neurocomputing 338 (2019), Jan. 22, 2019, https://www.sciencedirect.com/science/article/abs/pii/S0925231219300566?via%3D…
[cited by applicant]
Long et al., “Fully Convolutional Networks for Semantic Segmentation,” arXiv:1411.4038v2 [cs.CV] Mar. 8, 2015, 10 pages.
[cited by applicant]
Mondal et al., “Revisiting CycleGAN for semi-supervised segmentation,” arXiv:1908.11569v1 [cs.CV] Aug. 30, 2019, 13 pages.
[cited by applicant]
O'Shea et al., “An Introduction to Convolutional Neural Networks,” arXiv:1511.08458v2 [cs.NE] Dec. 2, 2015, 11 pages.
[cited by applicant]
Ronneberger et al., “U-Net: Convolutional Networks for Biomedical Image Segmentation,” arXiv:1505.04597v1 [cs.CV] May 18, 2015, 8 pages.
[cited by applicant]
Salton et al., “Introduction to Modern Information Retrieval,” McGraw-Hill Computer Science Series, Oct. 1986, 15 pages. (Table of Contents and Preface only) (Table of Contents and Preface taken from 1983 edition).
[cited by applicant]
Yang et al., “Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection,” IEEE Transactions on Intelligent Transportation Systems, Under Review, arXiv:1901.06340v2 [cs.CV] Jan. 25, 2019, 11 pages.
[cited by applicant]
Yi-De et al., “Automated image segmentation using improved PCNN model based on cross-entropy,” Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, Oct. 20-22, 2004, 5 page…
[cited by applicant]
Zhang et al., “Road Crack Detection Using Deep Convolutional Neural Network,” IEEE, ICIP 2016, Aug. 19, 2016, https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7533052, pp. 3708-3712.
[cited by applicant]
Zhu et al., “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks,” arXiv:1703.10593v7 [cs.CV] Aug. 24, 2020, 18 pages.
[cited by applicant]
Zou et al., “DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection,” IEEE Transactions on Image Processing, vol. 28, Issue: 3, Mar. 2019, https://ieeexplore.ieee.org/document/8517148, 15 pages.
[cited by applicant]