PreAugNet: improve data augmentation for industrial defect classification with small-scale training data

Author:

Farady Isack,Lin Chih-YangORCID,Chang Ming-Ching

Funder

Ministry of Science and Technology, Taiwan

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Software

Reference65 articles.

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2. Abu, M., Amir, A., Lean, Y., Zahri, N., & Azemi, S. (2021). The performance analysis of transfer learning for steel defect detection by using deep learning. Journal of Physics: Conference Series, 1755(1), 012041.

3. Bowles, C., Chen, L., Guerrero, R., Bentley, P., Gunn, R., Hammers, A., Dickie, D. A., Hernández, M. V., Wardlaw, J., & Rueckert, D. (2018). Gan augmentation: Augmenting training data using generative adversarial networks. Preprint retrieved from https://arxiv.org/abs/1810.10863.

4. Burrascano, P. (1991). Learning vector quantization for the probabilistic neural network. IEEE Transactions on Neural Networks, 2(4), 458–461.

5. Buslaev, A., Iglovikov, V. I., Khvedchenya, E., Parinov, A., Druzhinin, M., & Kalinin, A. A. (2020). Albumentations: Fast and flexible image augmentations. Information, 11(2), 125.

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