ГЕНЕРАТИВНІ ЗМАГАЛЬНІ МЕРЕЖІ ТА ЇХ ЗАСТОСУВАННЯ
Keywords:
Loss Function, Generator, Discriminator, Generative Adversarial Neural NetworkAbstract
The paper is devoted to consideration of the main tendencies of development of modern generative adversarial networks (GAN) and directions of their practical applications. Some features of creating such networks for improving the quality of images obtained by X-ray and computer tomography are presented. Due to the modification of the loss function due to the consideration of the competition component, the problem of improving the clarity of the image is preserved with the preservation of important details.
References
Dinh L., Sohl-Dickstein J., Bengio, S. (2016). Density estimation using real nvp. arXiv preprint arXiv:1605.08803.
Brock A., Lim T., Ritchie JM., Weston N. (2016) Neural photo editing with introspective adversarial networks. arXiv preprint arXiv:1609.07093.
Arjovsky M., Bottou L. (2017). Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862.
Che T., Li Y., Jacob AP., Bengio Y., Li W. (2016). Mode regularized generative adversarial networks. arXiv preprint arXiv:1612.02136, 2016.
Finn C. , Levine S. (2016). Deep visual foresight for planning robot motion. arXiv preprint arXiv:1610.00696 .
Donahue J., Krahenbuhl P., Darrell T. (2016). Adversarial feature learning. arXiv preprint arXiv:1605.09782 .
Dumoulin V., Belghazi I., Poole B., Lamb A., Arjovsky M., Mastropietro O., Courville A. (2016). Adversarially learned inference. arXiv preprint arXiv:1606.00704.
Lotter W., Kreiman G., Cox D. (2015). Unsupervised learning of visual structure using predictive generative networks. arXiv preprint arXiv:1511.06380.
Isola P., Zhu J.-Y., Zhou T., Efros A. A. (2016). Image-to-image translation with conditional adversarial networks. arXiv preprint arXiv:1611.07004.
