Advancements in Synthetic Data Extraction for Industrial Injection Molding
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-49011-8_43
Reference16 articles.
1. Almasri, W., Bettebghor, D., Adjed, F., Ababsa, F., Danglade, F.: Gmcad: an original synthetic dataset of 2d designs along their geometrical and mechanical conditions. Procedia Comput. Sci. 200, 337–347 (2022)
2. Goodfellow, I.: Nips 2016 tutorial: Generative adversarial networks (2016). arXiv:1701.00160
3. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)
4. Gupta, A., Vedaldi, A., Zisserman, A.: Synthetic data for text localisation in natural images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2315–2324 (2016)
5. Khomenko, M., Veligorskyi, O., Chakirov, R., Vagapov, Y.: An ann-based temperature controller for a plastic injection moulding system. Electronics 8(11), 1272 (2019)
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