Improving generative adversarial network inversion via fine-tuning GAN encoders

Author:

Yu Cheng,Wang WenminORCID,Bugiolacchi Roberto

Funder

Natural Science Foundation Project of Chongqing

Publisher

Elsevier BV

Reference44 articles.

1. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative Adversarial Nets, in: Proc. Adv. Neural Inf. Process. Syst., NeurIPS, 2014, pp. 2672–2680.

2. A. Radford, L. Metz, S. Chintala, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, in: Int. Conf. Learn. Representations, ICLR, 2016.

3. T. Karras, T. Aila, S. Laine, J. Lehtinen, Progressive Growing of GANs for Improved Quality, Stability, and Variation, in: Int. Conf. Learn. Representations, ICLR, 2018.

4. A. Brock, J. Donahue, K. Simonyan, Large Scale GAN Training for High Fidelity Natural Image Synthesis, in: Int. Conf. Learn. Representations, ICLR, 2019.

5. J. Deng, W. Dong, R. Socher, L. Li, K. Li, F. Li, ImageNet: A large-scale hierarchical image database, in: Proc. IEEE Conf. Comput. Vis. Pattern Recognition, CVPR, 2009, pp. 248–255.

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