[1] [1] Y. Tokozume, Y. Ushiku, T. Harada, Learning from betweenclass examples f deep sound recognition, in: Proc. of the 6th Intl. Conf. on Learning Representations, Vancouver, Canada, 2018, pp. 1–13.

[2] [2] R. Takahashi, T. Matsubara, K. Uehara, RICAP: Rom image cropping patching data augmentation f deep CNNs, in: Proc. of the 10th Asian Conf. on Machine Learning, Beijing, China, 2018, pp. 786–798.

[3] [3] I.J. Goodfellow, J. PougetAbadie, M. Mirza, et al., Generative adversarial s, in: Proc. of the 27th Intl. Conf. on Neural Infmation Processing Systems, Montreal, Canada, 2014, pp. 2672–2680.

[4] [4] A. Antoniou, A. Stkey, H. Edwards, Data augmentation generative adversarial wks [Online]. Available, https:arxiv.gabs1711.04340, November 2017.

[5] Lake B.M., Salakhutdinov R., Tenenbaum J.B.. Human-level concept learning through probabilistic program induction. Science, 350, 1332-1338(2015).

[6] [6] A. Radfd, L. Metz, S. Chintala, Unsupervised representation learning with deep convolutional generative adversarial wks, in: Proc. of the 4th Intl. Conf. on Learning Representations, San Juan, America, 2016, pp. 1–15.

[7] [7] M. FridAdar, E. Klang, M. Amitai, J. Goldberger, H. Greenspan, Synthetic data augmentation using GAN f improved liver lesion classification, in: Proc. of the 15th IEEE Intl. Symposium on Biomedical Imaging, Washington, America, 2018, pp. 289–293.

[8] [8] C. Han, K. Murao, T. Noguchi, et al., Learning me with less: Conditional PGGANbased data augmentation f brain metastases detection using highlyrough annotation on MR images, in: Proc. of the 28th ACM International Conference on Infmation Knowledge Management (CIKM ''19). Association f Computing Machinery, Beijing, China, 2019, pp. 119–127.

[9] [9] J. Redmon, A. Farhadi, YOLOv3: An incremental improvement [Online]. Available, https:arxiv.gabs1804.02767, April 2018.

[10] [10] X.Y. Zhu, Y.F. Liu, J.H. Li, T. Wan, Z.C. Qin, Emotion classification with data augmentation using generative adversarial wk, in: Proc. of the 22nd PacificAsia Conf. on Knowledge Discovery Data Mining, Melbourne, Australia, 2018, pp. 349–360.

[11] [11] J.Y. Zhu, T. Park, P. Isola, A.A. Efros, Unpaired imagetoimage translation using cycleconsistent adversarial wks, in: Proc. of IEEE Intl. Conf. on Computer Vision, Venice, Italy, 2017, pp. 2242–2251.

[12] [12] T. Kim, M. Cha, H. Kim, J.K. Lee, J. Kim, Learning to discover crossdomain relations with generative adversarial wks, in: Proc. of the 34th Intl. Conf. on Machine Learning, Sydney, Australia, 2017, pp. 1857–1865.

[13] [13] Z.L. Yi, H. Zhang, P. Tan, M.L. Gong, DualGAN: Unsupervised dual learning f imagetoimage translation, in: Proc. of IEEE Intl. Conf. on Computer Vision, Venice, Italy, 2017, pp. 2868–2876.

[14] Hu J., Shen L., Albanie S., Sun G., Wu E.-H.. Squeeze-and-excitation networks. IEEE T. Pattern Anal., 42, 2011-2023(2020).

[15] [15] Z. Liang, J.X. Huang, CycleGAN with dynamic criterion f malaria blood cell image synthetization, in: Proc. of AMIA Jt Summits Transl Sci Proc, Online, 2022, pp. 323–330.

[16] [16] S. Benaim, L. Wolf, Onesided unsupervised domain mapping, in: Proc. of the 31st Intl. Conf. on Neural Infmation Processing Systems, Long Beach, America, 2017, pp. 752–762.

[17] [17] B. Liu, J. Lv, X. Fan, et al., Application of an improved DCGAN f image generation, Mobile Infmation Systems 2022 (July 2022) 1–14.

[18] [18] M. Arjovsky, S. Chintala, L. Bottou, Wasserstein generative adversarial wks, in: Proc. of the 34th Intl. Conf. on Machine Learning, Sydney, Australia, 2017, pp. 214–223.

[19] [19] I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, A. Courville, Improved training of wasserstein GANs, in: Proc. of the 31st Intl. Conf. on Neural Infmation Processing Systems, Long Beach, America, 2017, pp. 5769–5779.