References
- LeCUN, Y. – BENGIO, Y., – HINTON, G.: (2015). Deep learning. nature, 521(7553), 436-444.
- GOODFELLOW, I. – POUGET-ABADIE, J. – MIRZA, M. – XU, B. – WARDE-FARLEY, D. – OZAIR, S.– BENGIO, Y.: (2020). Generative adversarial networks. Communications of the ACM, 63(11), 139-144.
- FRID-ADAR, M. – DIAMANT, I. – KLANG, E. – AMITAI, M. – GOLDBERGER, J. – GREENSPAN, H.: (2018). GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification, Neurocomputing, 321, 321–331.
- LU, C. Y. – RUSTIA, D. J. A. – LIN, T. T.: (2019). Generative adversarial network based image augmentation for insect pest classification enhancement. IFAC-PapersOnLine, 52(30), 1-5.
- MARIANI, G. – SCHEIDEGGER, F. – ISTRATE, R. – BEKAS, C. – MALOSSI, C.: (2018). Bagan: Data augmentation with balancing gan, arXiv preprint arXiv:1803.09655.
- ALBAWI, S., ABED MMOHAMMED, T. – ALZAWI, S.: (2017). Understanding of a convolutional neural network, 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey, pp. 1-6, doi: 10.1109/ICEngTechnol.2017.8308186.
- O’SHEA, K. – NASH, R.: (2015). An introduction to convolutional neural networks, arXiv preprint arXiv:1511.08458.
- RADFORD, A. – METZ, L. – CHINTALA, S.: (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434.
- ANTONIOU, A. – STORKEY, A. – EDWARDS, H.: (2017). Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340.
- ARJOVSKY, M. – CHINTALA, S. – BOTTLOU, L.: (2017, July). Wasserstein generative adversarial networks. In International conference on machine learning (pp. 214-223). PMLR.
- SOARES, E. – ANGELOV, P. – BIASO, S. – FROES, M. H. – ABE, D. K.: (2020). SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification. MedRxiv, 2020-04.
- SOARES, E. – ANGELOV, P. – BIASO, S. – FROES, M. H. – ABE, D. K.: (2020). SARS-COV-2 Ct-Scan Dataset, Available at: https://www.kaggle.com/datasets/plameneduardo/sarscov2-ctscan-dataset (accessed on 12th of August 12, 2024)
- CHAKRABARTY, N.: (2019). Brain MRI Images for Brain Tumor Detection, Available at: https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection (accessed on 12th of August 12, 2024)
- ACKERMANN, S. – SCHAWINSKI, K. – ZHANG, C. – WEIGEL, A. K. – TURP, M. D.: (2018). Using transfer learning to detect galaxy mergers. Monthly Notices of the Royal Astronomical Society, 479(1), 415-425.
- SCHAWINSKI, K.: (2018). Transfer Learning, Available at: https://github.com/SpaceML/merger_transfer_learning (accessed on 12th of August 12, 2024)
- BECKER, R. H. – WHITE, R. L. – Helfand, D. J.: (1995). The FIRST survey: faint images of the radio sky at twenty centimeters. Astrophysical Journal v. 450, p. 559, 450, 559.
- KORTSTRöM, J. – USKI, M. – TIIRA, T.: (2016). Automatic classification of seismic events within a regional seismograph network. Computers and Geo-sciences, 87, 22-30.
